The document describes a proposed smart crosswalk system that uses machine learning and image processing to monitor pedestrians and vehicles. It has four main components: 1) a real-time pedestrian detection and priority system customized for individuals with special needs, 2) a system to detect road conditions, vehicle availability and speed, 3) a real-time emergency vehicle detection and priority system, and 4) a system to identify pedestrian accidents and violations of crosswalk rules. The overall aim is to enhance pedestrian safety and traffic flow.
Automated signal pre-emption system for emergency vehicles using internet of ...IAESIJAI
Vehicle administration systems are one of the major highlights especially in urban areas. One important critical component that requires attention are signal preemption systems. Every single work on traffic congestion identification either requires prior learning or long time to distinguish and perceive the closeness of congestion. FutureSight performs predictive analysis and control of traffic signals through the application of machine learning to aide ambulances in such a way that, a signal turns green beforehand so as to ensure an obstacle free path to the ambulance from source to destination based on various parameters such as traffic density, congestion length, previous wait times, arrival time thereby eliminating the need for human intervention. The method allows flexible interface to the driver to enter the hospital details to reach the destination with in time. The app then plans out the fastest route from the pickup spot to the selected hospital and sends this route to the system. The system then predict the amount of time that is required by the signal to remain green so as to clear all traffic at that specific junction before the ambulance arrives at that location.
The International Journal of Management Research and Business Strategy is a international journal in English published every day in our life. It offers a fast publication schedule of maintaining rigorous peer review..The use of recommended electronic formats for article delivery the process and submitted research review articles and Case Studies are subjected to immediate screening by the editors.
Integrated tripartite modules for intelligent traffic light systemIJECEIAES
The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.
Online Accessable Traffic Control System for Urban Areas Using Embedded Syste...IJSRD
During recent years traffic congestion is become a serious problem in almost all cities. Due to the high density of traffic, pedestrians find it difficult to cross the road. Even though several advanced strategic plans are introduced to regulate the traffic but due to lack of provision for on- road pedestrian crossing, rate of accidents become very high. One such provision is given is elevated path for pedestrian to cross the road, but the elderly person finds it difficult to use that. Hence an idea is proposed to help the elderly people by giving provision for on- road pedestrian crossing in high density traffic areas like near schools, hospitals, markets, etc. which reduces the accidents rate also. To implement this, here an additional time delay is introduced in the traffic signal for pedestrian crossing in addition to vehicle crossing in all possible direction. Additionally, provision is given to track the vehicle which violates the traffic rules and to clear the traffic for emergency vehicles. All the above said three parameters can be simulated by using PROTEUS software.
Public transport service is one of the most preferred
modes of transportation in today’s smart cities. People prefer
public transport mainly for the cost benefit reasons. The
problems faced by the people while using the public transport
can be overcome by the technology such as Internet of Things
(IOT). In this paper, we present how this technology can be
applied to eliminate the problems faced by the passengers of the
public bus transport service. The Internet of Things technology is
used to provide the passengers waiting at the bus stop with real
time information of the arriving buses. Information such as
arrival time, crowd density and traffic information of the
arriving buses are predetermined and provided to the passengers
waiting at the bus stop. The display boards fitted at the bus stops
provide the real time bus navigation information to the waiting
passengers. This Smart Bus Navigation system enables the
passengers to make smart decisions regarding their bus journey.
This system reduces the anxiety and the waiting time of the
passenger’s at the bus stop. The smart bus navigation system
creates a positive impact and increases the number of people who
prefer to use the public mode of transportation.
Techniques for Smart Traffic Control: An In-depth ReviewEditor IJCATR
Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted
scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS)
to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle
traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of
these systems in use today for road traffic management. They include – fuzzy expert systems (FES), artificial neural networks (ANN)
and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper
cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of
the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just
routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a
cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with
respect to the context in which each of them is used.
Automated signal pre-emption system for emergency vehicles using internet of ...IAESIJAI
Vehicle administration systems are one of the major highlights especially in urban areas. One important critical component that requires attention are signal preemption systems. Every single work on traffic congestion identification either requires prior learning or long time to distinguish and perceive the closeness of congestion. FutureSight performs predictive analysis and control of traffic signals through the application of machine learning to aide ambulances in such a way that, a signal turns green beforehand so as to ensure an obstacle free path to the ambulance from source to destination based on various parameters such as traffic density, congestion length, previous wait times, arrival time thereby eliminating the need for human intervention. The method allows flexible interface to the driver to enter the hospital details to reach the destination with in time. The app then plans out the fastest route from the pickup spot to the selected hospital and sends this route to the system. The system then predict the amount of time that is required by the signal to remain green so as to clear all traffic at that specific junction before the ambulance arrives at that location.
The International Journal of Management Research and Business Strategy is a international journal in English published every day in our life. It offers a fast publication schedule of maintaining rigorous peer review..The use of recommended electronic formats for article delivery the process and submitted research review articles and Case Studies are subjected to immediate screening by the editors.
Integrated tripartite modules for intelligent traffic light systemIJECEIAES
The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.
Online Accessable Traffic Control System for Urban Areas Using Embedded Syste...IJSRD
During recent years traffic congestion is become a serious problem in almost all cities. Due to the high density of traffic, pedestrians find it difficult to cross the road. Even though several advanced strategic plans are introduced to regulate the traffic but due to lack of provision for on- road pedestrian crossing, rate of accidents become very high. One such provision is given is elevated path for pedestrian to cross the road, but the elderly person finds it difficult to use that. Hence an idea is proposed to help the elderly people by giving provision for on- road pedestrian crossing in high density traffic areas like near schools, hospitals, markets, etc. which reduces the accidents rate also. To implement this, here an additional time delay is introduced in the traffic signal for pedestrian crossing in addition to vehicle crossing in all possible direction. Additionally, provision is given to track the vehicle which violates the traffic rules and to clear the traffic for emergency vehicles. All the above said three parameters can be simulated by using PROTEUS software.
Public transport service is one of the most preferred
modes of transportation in today’s smart cities. People prefer
public transport mainly for the cost benefit reasons. The
problems faced by the people while using the public transport
can be overcome by the technology such as Internet of Things
(IOT). In this paper, we present how this technology can be
applied to eliminate the problems faced by the passengers of the
public bus transport service. The Internet of Things technology is
used to provide the passengers waiting at the bus stop with real
time information of the arriving buses. Information such as
arrival time, crowd density and traffic information of the
arriving buses are predetermined and provided to the passengers
waiting at the bus stop. The display boards fitted at the bus stops
provide the real time bus navigation information to the waiting
passengers. This Smart Bus Navigation system enables the
passengers to make smart decisions regarding their bus journey.
This system reduces the anxiety and the waiting time of the
passenger’s at the bus stop. The smart bus navigation system
creates a positive impact and increases the number of people who
prefer to use the public mode of transportation.
Techniques for Smart Traffic Control: An In-depth ReviewEditor IJCATR
Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted
scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS)
to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle
traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of
these systems in use today for road traffic management. They include – fuzzy expert systems (FES), artificial neural networks (ANN)
and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper
cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of
the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just
routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a
cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with
respect to the context in which each of them is used.
Abstract—This paper provides a brief overview of the Intelligent Traffic Management System based on Artificial
Neural Networks (ANN). It is being utilized to enhance the present traffic management system and human resource
reliance. The most basic problem with the current traffic lights is their dependency on humans for their working.
The technologies used in the making of this automated traffic lights are Internet of Things, Machine Learning and
Artificial Intelligence. The basic steps used in Internet of Things are reported along with different ANN trainings.
This ANN model can be used for the minimization of traffic on roads and less waiting time at traffic lights. As a
result, we can make traffic lights more automated which in turn eventually deceases our dependency on human
resources
A VISION-BASED REAL-TIME ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM USING VEHICULA...JANAK TRIVEDI
In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.
Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.
Smart Road Technology for Traffic Management and ITS Infrastructure Assessmen...IJAEMSJORNAL
This technical work describe infrastructure requirement and the working principles and procedures involved in operation of a Smart Road. A Smart Road is similar to a conventional highway but the difference is, it is equipped with the electronic gadgets required to capture static and dynamic physical entities occupied on the road at a given time and location. Nowadays traffic safety and highway congestion has become a serious concern to the Authorities and required to be managed them within the available resources. Also it is not possible to increase the capacity of highway infrastructure to compete with increase in traffic. In cities on highway system, large amount of traffic data being generated and an integrated approach is required for the efficient management transportation system. Smart Road is an innovative approach wherein Information Communication Technologies (ICT) is merged with traditional infrastructure and integrated with digital technologies. Critical examination of literature review reveals that many technologies are available for data capturing and management. Notable among them are by using ultrasonic sensors, light sensors, motion sensors, camera and IOT devices. The data collected by the devices would be managed through cloud computing and big data analytic methods. To assess the current traffic situation spot speeds and traffic volumes are captured for peak and non-peak on the Express Highway and from the data captured 85th percentile speed and LoS are estimated. Smart road technology is discussed for transportation system management. And IT infrastructure requirement for capturing traffic related data demonstrated for the selected road in Muscat.
Our journal has been unwavering commitment to showcasing cutting-edge research. The journal provides a platform for researchers to disseminate their work on next-generation technologies. In an era where innovation is the driving force behind progress, JST plays a crucial role in shaping the discourse on emerging technologies, thus contributing to their rapid development and implementation.
Embedding Intelligent System on Ambulance and Traffic Monitoringijtsrd
Ambulance service is one of the crucial services that should not get delay. To overcome this situation this paper describes a solution that “ embedding Intelligent system on Ambulance and Traffic monitoring †which includes alerting and tracking mechanism with traffic light regulating such that the ambulance can achieve a free way as fast as possible. An algorithm is used to control the traffic signals automatically based on the key pressed by the driver from keyboard in the ambulance. The information reading the current as well as future location of ambulance is sent from the ambulance itself. This information is used to optimally control the traffic. The performance of the embedding intelligent system on Ambulance and Traffic monitoring is compared with the Fixed Mode Traffic Light Controller. It is observed that the proposed model is more efficient than the conventional controller in respect of less waiting time, more distance traveled by average vehicles and efficient operation during emergency mode and GSM interface. Moreover, the designed system has simple architecture, fast response time, user friendliness and scope for further expansion. D. Devi Kalyani | K. Syamal | Sk. Basheeramma | T. V. V. Ratna | Subodh panda "Embedding Intelligent System on Ambulance & Traffic Monitoring" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30747.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30747/embedding-intelligent-system-on-ambulance-and-traffic-monitoring/d-devi-kalyani
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...gerogepatton
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2024) provides a
forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors
are solicited to contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following areas, but
are not limited to these topics only.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
More Related Content
Similar to SMART CROSSWALK: MACHINE LEARNING AND IMAGE PROCESSING BASED PEDESTRIAN AND VEHICLE MONITORING SYSTEM
Abstract—This paper provides a brief overview of the Intelligent Traffic Management System based on Artificial
Neural Networks (ANN). It is being utilized to enhance the present traffic management system and human resource
reliance. The most basic problem with the current traffic lights is their dependency on humans for their working.
The technologies used in the making of this automated traffic lights are Internet of Things, Machine Learning and
Artificial Intelligence. The basic steps used in Internet of Things are reported along with different ANN trainings.
This ANN model can be used for the minimization of traffic on roads and less waiting time at traffic lights. As a
result, we can make traffic lights more automated which in turn eventually deceases our dependency on human
resources
A VISION-BASED REAL-TIME ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM USING VEHICULA...JANAK TRIVEDI
In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.
Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.
Smart Road Technology for Traffic Management and ITS Infrastructure Assessmen...IJAEMSJORNAL
This technical work describe infrastructure requirement and the working principles and procedures involved in operation of a Smart Road. A Smart Road is similar to a conventional highway but the difference is, it is equipped with the electronic gadgets required to capture static and dynamic physical entities occupied on the road at a given time and location. Nowadays traffic safety and highway congestion has become a serious concern to the Authorities and required to be managed them within the available resources. Also it is not possible to increase the capacity of highway infrastructure to compete with increase in traffic. In cities on highway system, large amount of traffic data being generated and an integrated approach is required for the efficient management transportation system. Smart Road is an innovative approach wherein Information Communication Technologies (ICT) is merged with traditional infrastructure and integrated with digital technologies. Critical examination of literature review reveals that many technologies are available for data capturing and management. Notable among them are by using ultrasonic sensors, light sensors, motion sensors, camera and IOT devices. The data collected by the devices would be managed through cloud computing and big data analytic methods. To assess the current traffic situation spot speeds and traffic volumes are captured for peak and non-peak on the Express Highway and from the data captured 85th percentile speed and LoS are estimated. Smart road technology is discussed for transportation system management. And IT infrastructure requirement for capturing traffic related data demonstrated for the selected road in Muscat.
Our journal has been unwavering commitment to showcasing cutting-edge research. The journal provides a platform for researchers to disseminate their work on next-generation technologies. In an era where innovation is the driving force behind progress, JST plays a crucial role in shaping the discourse on emerging technologies, thus contributing to their rapid development and implementation.
Embedding Intelligent System on Ambulance and Traffic Monitoringijtsrd
Ambulance service is one of the crucial services that should not get delay. To overcome this situation this paper describes a solution that “ embedding Intelligent system on Ambulance and Traffic monitoring †which includes alerting and tracking mechanism with traffic light regulating such that the ambulance can achieve a free way as fast as possible. An algorithm is used to control the traffic signals automatically based on the key pressed by the driver from keyboard in the ambulance. The information reading the current as well as future location of ambulance is sent from the ambulance itself. This information is used to optimally control the traffic. The performance of the embedding intelligent system on Ambulance and Traffic monitoring is compared with the Fixed Mode Traffic Light Controller. It is observed that the proposed model is more efficient than the conventional controller in respect of less waiting time, more distance traveled by average vehicles and efficient operation during emergency mode and GSM interface. Moreover, the designed system has simple architecture, fast response time, user friendliness and scope for further expansion. D. Devi Kalyani | K. Syamal | Sk. Basheeramma | T. V. V. Ratna | Subodh panda "Embedding Intelligent System on Ambulance & Traffic Monitoring" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30747.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30747/embedding-intelligent-system-on-ambulance-and-traffic-monitoring/d-devi-kalyani
Similar to SMART CROSSWALK: MACHINE LEARNING AND IMAGE PROCESSING BASED PEDESTRIAN AND VEHICLE MONITORING SYSTEM (20)
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...gerogepatton
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2024) provides a
forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors
are solicited to contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following areas, but
are not limited to these topics only.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)gerogepatton
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Co...gerogepatton
Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
10th International Conference on Artificial Intelligence and Soft Computing (...gerogepatton
10th International Conference on Artificial Intelligence and Soft Computing (AIS 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology, and
applications of Artificial Intelligence, Soft Computing. The Conference looks for significant
contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical
aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from
both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AIFU 2024) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...gerogepatton
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
SMART CROSSWALK: MACHINE LEARNING AND IMAGE PROCESSING BASED PEDESTRIAN AND VEHICLE MONITORING SYSTEM
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
DOI:10.5121/ijaia.2023.14603 21
SMART CROSSWALK: MACHINE LEARNING AND
IMAGE PROCESSING BASED PEDESTRIAN AND
VEHICLE MONITORING SYSTEM
Hiruni J.M.D.K, Weerakoon L.M.R, Weerasinghe T.R, Jayasinghe
S.J.A.S.M.S, Jenny Krishara, Sanjeevi Chandrasiri
Department of Computer Science and Software Engineering, Sri Lanka Institute of
Information Technology, Malabe, Sri Lanka
ABSTRACT
The conventional pedestrian crossing system's shortcomings require urgent reform to enhance the safety of
pedestrians and improve urban mobility. Issues such as insufficient time for pedestrians to cross, prolong
waiting times, neglection of emergency vehicles, and the absence of effective 24/7 response mechanisms at
traditional crosswalks present significant safety concerns in urban areas. Our primary intention is to
develop a cutting-edge pedestrian crossing system that relies on deep learning and image processing
technologies as its foundation. This research addresses to innovate an advanced smart crosswalk
consisting of four essential components: a real-time Pedestrian Detection and Priority System customized
for individuals with special needs, a responsive system for detecting road conditions, vehicle availability
and speed near crosswalks, a real-time Emergency Vehicle Detection and Priority System strengthened by
rigorous verification procedures, and a robust framework for identifying pedestrian accidents and
violations of crosswalk rules. The entire system has been meticulously designed not only to enhance
pedestrian safety by identifying potential dangers but also to optimize traffic flow. In essence, it aims to
provide an improved pedestrian crossing experience characterized by increased safety and efficiency.
KEYWORDS
Pedestrian Safety, Image Processing, Machine Learning, Deep Learning, YOLO
1. INTRODUCTION
Pedestrian safety presents a pressing challenge in today's urban transportation systems, with
crosswalk accidents and fatalities posing significant problems for cities worldwide. A primary
contributor to these issues is the lack of coordination between pedestrians and vehicles,
emphasizing the need for advanced safety solutions. Data from the "Pedestrian safety, A road
safety manual for decision-makers and practitioners" [1] reveals that pedestrians account for
more than 20% of the annual 1.24 million traffic-related fatalities, highlighting the necessity for
targeted interventions.
The urban landscape surrounding traditional crosswalks is fraught with immediate concerns.
Pedestrians often grapple with insufficient time to safely crossroads, a predicament amplified for
individuals with special needs. The lack of attention to these vulnerable populations underscores
the demand for impartial solutions. Furthermore, the common occurrence of pedestrians waiting
near crosswalks in the absence of vehicular traffic leads to chronic time wastage, hindering urban
productivity and causing frustration among citizens. Neglecting to prioritize emergency vehicles
like ambulances at these points poses a substantial risk, potentially resulting in avoidable
accidents. The aftermath of vehicular-pedestrian accidents and widespread rule violations at
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
22
traditional crosswalks underscores a concerning absence of structured education and continuous
monitoring protocols. Addressing these multifaceted challenges at traditional crosswalks is
crucial for the safety, efficiency, and inclusivity of urban spaces.
The focal point of this research revolves around the innovative concept of a "Smart Crosswalk,"
which integrates Machine Learning, Deep Learning, and Image Processing techniques. This
system comprises four distinct components, each significantly enhancing crosswalk safety and
efficiency.
The primary emphasis is on optimizing pedestrian crossing times through real-time Pedestrian
Detection and Priority Systems. Customized to accommodate individuals with specific needs, this
approach utilizes algorithms to anticipate crossing times based on the number of individuals
present. The dynamic adjustment of pedestrian traffic light signals that results facilitates secure
and efficient pedestrian movement, promoting inclusivity and equitable access.
The research also delves into reducing pedestrian idling times and determining the optimal times
for pedestrians to cross the road. This is achieved through a responsive system that considers
real-time vehicle availability and uncontrollable speeds, departing from conventional static
systems to adjust signal timings according to dynamic traffic patterns.
In emergency scenarios, the research introduces a Real-time Emergency Vehicle Detection and
Priority System, reinforced by verification mechanisms. This technology swiftly identifies and
prioritizes emergency vehicles, minimizing disruptions to both pedestrian and vehicular traffic.
Additionally, the research addresses informing authorities about accidents and enforcing
pedestrian and vehicle rule violations. Employing image processing and machine learning
methodologies, the system identifies pedestrian accidents and rule violations, promoting safer
urban environments and reinforcing adherence to traffic regulations.
Each facet of the "Smart Crosswalk" system operates in synergy, embodying a comprehensive
approach to crosswalk safety and efficiency. Through the integration of computer vision, deep
learning, and advanced image processing techniques, this research seeks to bridge the divide
between technological innovation and urban well-being.
2. LITERATURE REVIEW
In urban settings, optimizing pedestrian safety and crosswalk efficiency is paramount. This
involves addressing challenges like inadequate crossing time, extended pedestrian waiting
periods, prioritizing emergency vehicles, and ensuring swift post-accident responses. This
literature review thoroughly examines these challenges, pinpoints areas where further research is
needed, and lays the groundwork for potential remedies. Tackling these issues is vital for
enhancing pedestrian safety, urban transportation, and the efficacy of relevant policies.
The study authored by Yuejin Wang et al. [2] introduces an automated image processing-based
system designed for pedestrian detection and enumeration. This system integrates various image
processing algorithms including background subtraction, Gaussian mixture model (GMM), and
blob analysis.
The research “Real Time Traffic Density Count Using Image Processing [3]” proposes an
algorithm to intelligently control traffic signals by determining the volume of the traffic on both
sides of the road. A density counting algorithm is used to compare real-time video frames to a
reference image and identify vehicles solely within a specific area of focus especially the road
area. The volume of the vehicles in the road is then used to control the traffic signal in a smart
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
23
manner by comparing it with the traffic density on other directions of the road. Here they have
used the vehicle count to examine the density of the roads.
The research “A Self-Adaptive Traffic Light Control System Based on Speed of Vehicles [4]”
present a system that utilizes V2I communication, whereby vehicles transmit their information
regarding the speed to the traffic lights and them controlling the traffic lights considering
situations. Using this data, the signal timings are dynamically adjusted in real-time with the aim
of optimizing vehicle flow across the intersection and lowering traffic congestion on main
roadways. Furthermore, the method used in this study relies on the presumption that the driver
can regulate the speed of the car. The technology has the potential to greatly decrease traffic and
enhance the security and dependability of transportation networks by utilizing real-time data and
non-orthogonal signals.
The traffic light control system designed by Bhoomika G M has shown the potential for using
image processing and neural networks to improve traffic management. The proposed ambulance
detection system builds on this work to identify and prioritize ambulances in traffic. By using
CNN and YOLOv5, the system can accurately detect ambulances even in crowded traffic
conditions. The system's ability to switch traffic signals green for 30 seconds to allow
ambulances to pass through intersections can significantly reduce the time it takes for ambulances
to reach their destinations, ultimately saving lives [5].
The research paper "Sound Sensors to Control Traffic System for Emergency Vehicles”
addresses urban ambulance congestion. Employing two wireless sound sensors with Xbee
protocol and Arduino, it detects ambulances at 100 meters, turning the relevant lane green for 2
minutes. This allows safe passage to the next sensor at the signal. After passage, the green signal
extends by 2 seconds for added safety. Cost-effective and adaptable for high-priority vehicles, the
system aids emergency responders, benefiting urban traffic flow [6].
In a study conducted by Hadi Ghahremannezhad et al. [7] a novel and efficient framework for
detecting accidents at junctions in traffic monitoring applications is introduced. This proposed
architecture comprises three hierarchical stages: precise and swift object identification through
the utilization of the YOLO_v4 technique, object tracking employing a Kalman filter in
conjunction with the Hungarian algorithm for association, and accident detection through
trajectory conflict analysis.
Marjan Simončič [8] conducted a separate study focusing on a collection of traffic incidents
involving various combinations of motor vehicles, pedestrians, bicycles, and motorcyclists in
Slovenia. Subsequently, the logistic regression technique was employed to scrutinize this specific
group of incidents.
A study by Jianqing-Wu et al. [9] focuses on potential collisions between pedestrians and moving
vehicles, a critical concern for pedestrian safety. Techniques such as object tracking, grouping,
classification, background filtering, and lane identification are employed. Three key indicators,
post encroachment time (PET), percentage of stopping distance (PSD), and crash possibility
index (CPI), are used to assess conflict risk. Case study results affirm the effectiveness of this
approach in identifying near-crash situations between pedestrians and vehicles.
The paper by J. Z. Zhang et al. [10] offers a comprehensive summary of current research
concerning pedestrian crossing detection and behavior analysis. It underscores a range of
methods and strategies employed in pedestrian identification, monitoring, and behavior
assessment.
4. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
24
A traffic light control system was proposed by Divij N, Divya K, and Anuradha Badage, which
aims to detect the siren sound of approaching emergency vehicles and prioritize their passage
through intersections. The proposed system integrates a sound detection sensor, camera, and
microcontroller into a smart object, which processes the data. To facilitate communication
between the smart objects and a centralized Decision Support System installed at the signal
junction, LoRa technology is used. By utilizing the Decision Support System, the system is able
to make informed decisions about clearing traffic in the lane where the emergency vehicle is
passing.
In the first phase of the proposed system, the smart object detects the emergency vehicle on the
road through sound detection sensor and camera. The smart object compares the moving object
on the road with the stored dataset to determine whether it is an emergency vehicle. The smart
object sends a message to the Decision Support System at the signal junction if both conditions
are met. In the system's second phase, the Decision Support System will determine the
appropriate course of action to ensure the emergency vehicle can safely navigate through the
traffic lane. The system is also equipped with acoustic sensors near the intersection, which work
on Receding Doppler Effect, to confirm the departure of the emergency vehicle [11].
Research by a group of Indian researchers, which is “Modelling pedestrian road crossing
behavior under mixed traffic conditions [12]” is a study that investigates road crossing patterns of
the pedestrians at midblock locations which are uncontrolled in India under contrasting traffic
conditions. In the research, linear regression techniques are applied to develop a model that
predicts the size of vehicular gaps on the road near pedestrian crossings. Additionally, a choice
model in statistics is used to analyze the decision-making process of pedestrians when it comes to
accepting or rejecting gaps between vehicles. The findings indicate that there are various aspects
that have a considerable impact on pedestrian safety at uncontrolled crossings, such as the
driver's willingness to yield, the size of the rolling gap, and the frequency of crossing attempts.
The findings of this study can be valuable in improving pedestrian safety at such crossings. This
research paper uses statistical models to determine the vehicle gaps on the road.
The research paper named “Automatic Traffic Using Image Processing [13]” proposes an
adaptive traffic light system that uses image processing and traffic density calculation as the
parameters. The system aims to address the issues of heavy traffic jams which occurred by
conventional traffic lights that work based on a timer. The system uses a server to collect data
and control traffic light mechanisms at a crossroads. Here the algorithms which are used for
vehicle density calculation and to adjust timing of traffic lights are validated and tested through
conditions on an actual road, and the results illustrates good accuracy in detecting traffic density
and successfully calculating the timing of traffic lights.
The research paper “Traffic Signal Violation Detection using Artificial Intelligence and Deep
Learning [14]”, which concentrates on the detection of traffic violations, proposed research
extends its focus to encompass both accident prevention and the enforcement of traffic rules.
While their work aims to detect various traffic infractions, such as signal jumping, speeding
vehicles, and vehicle counting, proposed research combines YOLO-based object detection with
custom YOLO models for accident detection and vehicle rule violation detection during red lights
at crosswalks. By addressing both accident prevention and rule enforcement simultaneously,
proposed research offers a more comprehensive solution to enhance pedestrian safety and
promote adherence to traffic regulations, ultimately contributing to a safer road environment for
both pedestrians and drivers.
Giovanni Pau et al.'s study, titled “Smart Pedestrian Crossing Management at Traffic Light
Junctions through a Fuzzy-Based Approach [15]”, delves into the imperative task of enhancing
5. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
25
pedestrian safety at signalized crossings. With urbanization and rapid population growth
presenting formidable challenges, the need for intelligent solutions in urban planning has never
been more apparent. Pau and his co-authors address this concern by leveraging Information and
Communications Technologies (ICT) to implement a fuzzy logic-based system. This system
dynamically adjusts traffic light phases, accounting for variables such as time of day and
pedestrian volume. Through rigorous analysis and simulation using Vissim, their work
significantly advances discussions surrounding urban mobility and pedestrian safety.
“A dynamic traffic light management system based on wireless sensor networks for the reduction
of the red-light running phenomenon [16]” research describes the problem of accidents at traffic
light junctions and the potential of Intelligent Transport Systems to improve safety of the roads in
these areas. In addition, the article states that conventional traffic light control systems may not
always be successful and can result in accidents, especially the risk of Red-Light Running. This
situation arises when drivers must decide whether to stop or keep driving through an intersection
as the state of the traffic light changes from green to yellow. If the vehicle driver does not stop at
a red-light signal, they are breaking the law and putting themselves and others in danger. Red
light running is a frequent reason for collisions at junctions with traffic lights. The authors
propose that real-time data from WSNs may be utilized to dynamically adjust traffic signal cycles
and monitor traffic volumes. The study makes the argument that incidences of red-light running
can be reduced by reducing the amount of time people have to wait at traffic signals. It describes
the intended structure in detail and assesses its efficiency.
The research paper “Density Based Traffic Control System Using Image Processing [17]”
presents a real-time dynamic traffic regulation system that utilizing methods of image processing
to tally the quantity of automobiles present in each stage of traffic light and allocate timings
accordingly. Here as the density, they have considered the number of vehicles present. A camera
has been installed to record footage of the highway. Each frame of the video is compared to the
original image that was captured as it is constantly recorded in successive frames. Image
processing algorithms are used to count vehicles.
Another study by Alisa-Makhmutova et al [18] “Intelligent Detection of Object’s Anomalies for
Road Surveilance Cameras”. This study focuses on computer vision and machine learning
approaches that enable applications to accomplish various tasks in real time without a human,
such as object recognition, anomaly detection, and incident detection. In this article, they
examined how our AI based object recognition and tracking model is affected by picture
preprocessing techniques like grayscaling. Additionally, they used machine learning algorithms
to find recurring object paths and anomalies in real-time footage from traffic surveillance
cameras. Based on this, they proposed a method to identify illegal trajectories being followed by
cars or people.
3. METHODOLOGY
Figure 1. High level overall system diagram
6. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
26
3.1. Real-Time Pedestrian Detection and Priority System for People with Special
Needs on Crosswalk
The research embarks with the collection of a diverse range of datasets, encompassing video
feeds of pedestrians in proximity to crosswalks. These datasets encapsulate a spectrum of
scenarios, including varying weather conditions, demographics, and urban settings. Preceding the
actual training process, extensive data preprocessing is undertaken. This includes video frame
extraction, resizing, and augmentation to ensure the quality and diversity of the dataset.
Importantly, the data is meticulously segregated for training and testing purposes.
The heart of the research lies in the selection and deployment of sophisticated models. The
YOLOv8 object detection framework is chosen as the backbone for precise pedestrian detection
due to its well-regarded real-time object recognition capabilities. Additionally, a distinct
YOLOv8s model is implemented for the specialized task of disabled pedestrian detection. The
selection of these models hinges on their proven performance under real-world conditions and
their versatility in addressing various pedestrian scenarios.
Crucial to the success of the system is the fine-tuning of these selected models using the collected
datasets. This training process involves the optimization of model parameters to enhance both
accuracy and overall performance. A notable aspect of this fine-tuning process is the special
emphasis placed on training the models to accurately identify mobility aids used by disabled
pedestrians, which include crutches, push wheelchairs, walking frames, and wheelchairs.
Upon completion of the model fine-tuning phase, the trained models are seamlessly integrated
into the pedestrian detection and time estimation system. This system is adept at processing video
feeds captured by high-angle CCTV cameras, enabling the real-time identification of pedestrians
and their associated mobility aids. It is of utmost importance that the system excels at detecting
and accurately categorizing disabled pedestrians, thereby ensuring their prioritization in the road-
crossing process.
In tandem with the detection system, the research encompasses the development of a precise
pedestrian counting mechanism. Employing advanced image processing techniques and mask
creation, the system can accurately quantify the number of pedestrians who are prepared to cross
the road. This data subsequently forms the basis for time estimation, a core component of the
research.
The time estimation aspect involves the creation of a machine learning-based algorithm capable
of estimating the time duration required for safe pedestrian crossing. This algorithm exhibits
remarkable adaptability, catering to diverse pedestrian demographics that encompass children,
adults, and particularly disabled individuals. It's noteworthy that the model takes into careful
consideration the specific mobility challenges faced by disabled pedestrians, thus prioritizing
their safety and convenience throughout the estimation process.
3.2. Real-Time Responsive System on Road Condition, Vehicle Availability, and
Uncontrollable Speeds Near Crosswalks
This research component encompasses the development of a dynamic traffic light control system
which is responsive to real-world data and traffic conditions. The objective is to minimize idle
time for pedestrians near pedestrian crossings through the integration of machine learning
methodologies, image processing, and sophisticated traffic control logics.
7. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
27
The foundation of this research is built upon the collection and annotation of image data.
Highangle CCTV camera footage is used to capture real-world traffic scenarios. The data
includes instances of various road conditions, such as "Bare Roads" and "Filled Roads," along
with the presence or absence of vehicles on the road. These 1000 data points are meticulously
annotated and pre-processed using augmentation methods and resized to train the machine
learning model effectively.
The core of the research methodology revolves around the utilization of the YOLOv8 machine
learning model. YOLOv8 serves as the primary tool for detecting and classifying road conditions
and the presence of vehicles in the CCTV footage. The model is trained to achieve high accuracy
in real-time object detection and classification, forming the basis for the subsequent traffic
control logics.
To distinguish between "Bare Roads" and "Filled Roads," a specialized methodology is
developed using YOLOv8. This classification is crucial in adapting traffic control measures to
real-time road conditions. The methodology is designed to work seamlessly with high-angle
CCTV camera footage, ensuring accurate road classification.
The "Vehicle Availability Confirmation Checker" is a critical component of the system where a
pretrained YOLOv8 model is used to detect vehicles in real-time and confirm their presence in
front of a predetermined limit line. This involves training the model on a dataset of frames with
labeled vehicle presence, enabling it to deliver "Available" or "Unavailable" results. The checker
plays a pivotal role in ensuring that pedestrian safety is a top priority in traffic control decisions.
Another vital aspect of the methodology is the "Uncontrollable Speed Detection" system. It is
designed to identify and measure vehicle speeds accurately. Vehicle speeds are measured using
two benchmark lines and using the equation,
Speed (m/s) = Distance(m) / Time(s)
This real-time speed detection system leverages the capabilities of the YOLOv8 model to identify
high-speed incidents, contributing to pedestrian safety and efficient traffic management.
The core of the traffic light control system consists of four intelligent traffic control logics. These
logics are executed within specific timeframes, responding to the results of the custom machine
learning model, the availability confirmation checker and the uncontrollable speeds.
• Traffic Control Logic 01: This logic ensures a predictable traffic signal pattern by
transitioning to red when both sides of the road are "Available" and "bare."
• Traffic Control Logic 02: Adaptability is the hallmark of this logic, which responds to the
presence or absence of vehicles in real-time, reducing idling time and optimizing traffic
flow.
• Traffic Control Logic 03: Addressing high-speed incidents, this logic remains green when
uncontrollable vehicle speed is detected, promoting pedestrian safety and reducing
accidents.
• Traffic Control Logic 04: Balancing efficiency and safety, this logic prioritizes pedestrians
right of way, while ensuring green traffic lights are maintained till the vehicle crosses the
crosswalk.
8. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
28
Figure 2. High level traffic light setup on road conditions
3.3. Real-Time Emergency Vehicle Detection and Priority System with Verification
for Crosswalk
This research component encompasses the development of a state-of-the-art smart crosswalk
system, with a primary objective of promptly identifying emergency vehicles, particularly
ambulances, and prioritizing their secure passage through congested crosswalks. The core
methodology revolves around the utilization of the YOLOv5 machine learning model, recognized
for its proficiency in real-time object detection tasks.
To achieve this objective, a comprehensive dataset of real-world urban traffic scenarios was
gathered. This dataset encompasses a diverse range of environmental conditions, traffic densities,
and emergency vehicle configurations. High-resolution images and corresponding metadata were
collected using strategically positioned cameras near intersections. The images were meticulously
annotated to highlight the presence and location of emergency vehicles, thus creating a labeled
dataset for model training. In total, 2500 data points were collected, ensuring a robust foundation
for model development and evaluation.
To initiate the process, two sound sensors are strategically positioned near the crosswalk to detect
the distinct siren sound emitted by approaching emergency vehicles. By employing advanced
triangulation techniques, the system accurately determines the direction of the sound source. This
auditory data is relayed to the system for further processing. The INMP441 Omnidirectional
Microphone Module, integrated via a 12S connection to the ESP32 Development Board, forms
the sensory backbone. The ESP32, acting as the central processing unit, leverages its WiFi and
Bluetooth capabilities for seamless networking. The Arduino IDE serves as the development
environment for programming and integrating these components. The ESP32 captures and
processes the audio data, first identifying the siren sound amidst ambient noise. Leveraging the
inherent capabilities of the INMP441 module and the processing power of the ESP32, the system
effectively hones in on emergency vehicle sirens.
9. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
29
Subsequently, the system employs machine learning algorithms to analyze real-time footage from
strategically positioned CCTV cameras at the crosswalk. This analysis serves a dual purpose:
firstly, to verify if the identified vehicle is indeed an ambulance, and secondly, to track its path
across the crosswalk. For ambulance verification, a fine-tuned YOLOv5 model is utilized,
ensuring accurate identification even in complex traffic scenarios. Once the ambulance presence
is confirmed, the system proceeds to the next step.
The system then leverages IoT components to ascertain the approaching direction of the
ambulance based on the detected siren sound. Through this integration, the system gains the
ability to precisely identify the angle from which the ambulance is approaching the crosswalk.
Upon confirmation of the ambulance's presence and its approaching direction, the system
communicates with the traffic control infrastructure, requesting priority passage. This
coordination synchronizes traffic lights and associated devices, ensuring a safe and unhindered
path for the ambulance. Simultaneously, the system maintains vigilant surveillance over the
ambulance's movement, ensuring it crosses the intersection smoothly. After a successful transit,
the system promptly switches the traffic lights to green, allowing pedestrians to cross without
undue delay, while still prioritizing their safety following the ambulance's passage.
3.4. Pedestrian Accident and Rule Violation Detection on Crosswalk
To create an effective accident detection model, a diverse dataset was assembled. It combined
real-world accident images, when accessible, and synthetic images generated through graphical
tools. Synthetic images were carefully designed to mimic real accident scenarios with varied
conditions. Each image was meticulously annotated with precise bounding boxes to label
pedestrians and vehicles involved in accidents. The dataset was thoughtfully divided into training
(70%), testing (20%), and validation (10%) sets to facilitate robust model development and
evaluation, preventing overfitting. This comprehensive dataset is essential for training and testing
the accident detection model.
The You Only Look Once (YOLO) architecture, specifically YOLOv5, was selected as the
foundation for accident detection. YOLOv5 is esteemed for its real-time object detection
capabilities and efficient use of resources. It adopts a single-stage approach to object detection,
making it suitable for applications requiring speed and accuracy. The architecture consists of a
backbone network, neck, and head, collectively responsible for efficient feature extraction, object
detection, and bounding box regression. The model employs anchor boxes to predict object
locations and confidence scores. The choice of YOLOv5 over other architectures is rooted in its
aptness for real-time accident detection within crosswalks, where time is of the essence.
To equip the YOLOv5 architecture for the task of pedestrian-vehicle accident detection within
crosswalks, a comprehensive training process was undertaken. The hyperparameters were
methodically fine-tuned to ensure optimal model convergence and accuracy. The learning rate
(α), batch size, anchor box dimensions, and confidence threshold were all meticulously adjusted.
Learning rate (α) was a critical component that significantly influenced the model's optimization
process. Through rigorous experimentation, a learning rate of α = 0.001 was empirically selected.
This value was integral in controlling the step size of each gradient descent iteration.
In conjunction with hyperparameter tuning, the training process involved the execution of
multiple epochs. With the synthetic dataset containing around 800 images, a crucial consideration
was preventing overfitting. Hence, the training strategy encompassed a total of 20 epochs,
thoughtfully chosen to strike a balance between convergence and generalization. This approach
10. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
30
prevented the model from becoming excessively tailored to the training data while allowing it to
effectively learn the underlying patterns necessary for accurate accident detection.
The custom loss function, incorporating object detection (L_obj), no-object detection (L_noobj),
classification (L_cls), and bounding box regression (L_reg), was meticulously defined to guide
the training process. The loss function was calculated as follows:
L(Θ) = (1 - β) * L_obj + β * L_noobj + L_cls + λ * L_reg
The parameter β, set to 0.5, ensured a balanced approach to object and no-object detection. This
balance was critical in maintaining the model's ability to distinguish objects and background
effectively. The coefficient λ, representing the influence of regression loss, was assigned a value
of 1.0, indicating the equal significance of precise bounding box regression in the context of
accident detection.
4. RESULTS AND DISCUSSIONS
The implementation of the Pedestrian Detection and Time Estimation System has generated
highly promising outcomes across its diverse components. Anchored by the YOLOv8 object
detection framework, the system achieves a notable accuracy rate of 93.87% in pedestrian
detection, marking its potential to significantly enhance urban traffic management through
responsive intersection control and heightened road safety standards.
Figure 3. Pedestrian detection
Figure 4. Confusion matrix of disabled pedestrians
11. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
31
Figure 4. Precision, recall, and overall accuracy
In the domain of disabled pedestrian identification, the system also excels with an accuracy rate
of 82.34%, effectively categorizing individuals based on their mobility aids to prioritize safety
and convenience during road crossing. Moreover, the pedestrian counting mechanism,
underpinned by advanced image processing, delivers precise counts and offers valuable insights
into pedestrian behavior patterns. The real-time time estimation algorithm, meticulously tailored
for different demographics.
The research outcomes underscore the efficacy of four distinct traffic control logics in optimizing
road conditions and ensuring pedestrian safety. In the initial phase between 0 to 50 seconds,
(Fig.6.) fosters predictability and safety by rapidly transitioning to red when both road conditions
are "bare" and the availability confirmation checker signals "Available." This logic aligns traffic
light behavior with real-world data, reducing unnecessary idling time and enhancing pedestrian
safety. In the first 50 seconds to 100 seconds (Fig. 7.), the system promptly turns traffic lights red
when both custom models concur on either "bare-roads" or “Filled Roads” and the availability
confirmation checker indicates "Available," when no vehicles are in sight. This ensures a
pedestrian-friendly environment and reduces idling time for pedestrians, aligning traffic lights
with road conditions and improving overall efficiency.
Between 100 to 120 seconds, (Fig.8.) introduces a dynamic response to uncontrollable vehicle
speed. When high-speed incidents are detected, the traffic lights remain green, optimizing traffic
flow efficiency while addressing the seriousness of high-speed scenarios. In the subsequent phase
after 120 seconds, (Fig.9.) strikes a balance between pedestrian crossings and safety. It permits
pedestrian crossings when road conditions are favourable and high-speed incidents are absent,
prioritizing both safety and mobility.
Figure 5. Traffic control logic 1
12. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
32
Figure 6. Traffic control logic 2
Figure 7. Traffic control logic 3
Figure 8. Traffic control logic 4
The ambulance detection component demonstrates its effectiveness in enhancing intersection
safety. The fine-tuned YOLOv5 model boasts an impressive 88% accuracy and a low 47% loss
rate. Leveraging confusion matrix (Fig.11.) and detection images (Fig.10.) provides valuable
performance insights. The integration of auditory cues via strategic sound sensors, supported by
advanced triangulation, ensures precise ambulance approach detection, strengthening the system's
efficacy. The system swiftly responds to an approaching ambulance, coordinating with IoT
components for a seamless, unobstructed passage. It monitors the ambulance's movement,
ensuring a smooth intersection crossing. Post-successful transit, traffic lights promptly shift to
green, enabling uninterrupted pedestrian crossing with continued priority for safety. This
approach not only expedites emergency response but also optimizes intersection operations,
greatly enhancing urban traffic safety.
13. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
33
Figure 9. Ambulance detection
Figure 10. Ambulance confusion matrix
In the evaluation of our accident detection model (Fig.12.), we achieved compelling results.
Notably, the model exhibited an impressive confidence level of 0.80 in detecting real-world
pedestrian accidents, reflecting its robustness. Furthermore, during testing, it demonstrated an
accuracy rate of about 86%, reinforcing its competence in accident detection. Key metrics, such
as precision (0.87), recall (0.85), and F1-score (0.86), emphasize the model's effectiveness in
minimizing both false positives and false negatives, quantifying its accident identification
capabilities. Importantly, with increasing training epochs, the model's performance improved,
peaking at 88% accuracy after 20 epochs, indicating its adaptability and potential for further
refinement. Additionally, with an average inference time of about 40 milliseconds per image, the
model is suitable for real-time traffic surveillance, contributing significantly to road safety and
accident prevention. The vehicle rule violation detection leverages a pre-trained YOLO model. It
involves masking the crosswalk area and continuously monitoring traffic light status, flagging
red-light violations, enhancing road safety (Fig.13.).
Figure 11. Accident detection
14. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
34
Figure 12. Rule violation when red
5. CONCLUSION AND FUTURE WORKS
In the realm of urban transportation, the emergence of the "Smart Crosswalk" system represents a
paradigm shift in addressing pedestrian safety concerns through innovative technological
solutions. The research outlined a comprehensive framework integrating advanced technologies
like deep learning and machine learning to revolutionize pedestrian safety, traffic management,
and urban mobility. The "Smart Crosswalk" system has demonstrated its efficacy in real-time
Pedestrian Detection, Priority Systems, adaptive vehicle detection, and Emergency Vehicle
Detection, showcasing its potential to enhance safety and efficiency in urban environments.
As the study looks to the future, several avenues for further exploration and improvement present
themselves. One crucial aspect is the augmentation of the dataset for pedestrian accidents, aiming
for diversity in scenarios, lighting conditions, and environmental factors. This expansion will
fortify the accident detection system's robustness, ensuring its effectiveness across a broader
range of real-world situations. Accurate time estimation for pedestrian crossings could be refined
through extended data collection, encompassing various pedestrian actions and environmental
conditions to enhance the model's accuracy.
Furthermore, the research suggests automating the identification of crosswalk areas and road
lanes through computer vision techniques. This would eliminate the need for manual
intervention, increasing the system's adaptability to different crosswalk configurations and
streamlining its implementation. User experience and accessibility considerations remain
paramount, with future research focusing on user studies and feedback collection, particularly
from pedestrians with special needs. Iterative improvements based on this feedback will enhance
the system's inclusivity and overall functionality.
In conclusion, the "Smart Crosswalk" system not only stands as a testament to the transformative
power of innovation in urban transportation but also serves as a foundation for ongoing research
and development. The suggested future work addresses key areas such as dataset augmentation,
time estimation accuracy, dynamic identification of crosswalk areas, and user experience
considerations. This collective effort aims to create a safer, more efficient, and inclusive urban
future, where technology seamlessly integrates with the well-being of all inhabitants. As cities
evolve, the "Smart Crosswalk" system offers a beacon of progress, inviting collaboration and
exploration for the continued advancement of urban safety and mobility.
REFERENCES
[1] W. H. Organization, “World Health Organization,” [Online].
Available: https://www.who.int/publications/i/item/pedestrian-safety-a-road-safety-manual-for-
decisionmakers-and-practitioners.
[2] N. Abbas, N. Abbas and T. M. Qadri, “Real Time Traffic Density Count using Image Processing”.
15. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.6, November 2023
35
[3] J. Li, Y. Zhang and Y. Chen, “A Self-Adaptive Traffic Light Control System Based on Speed of
Vehicles”.
[4] B. G. M, “Ambulance Detection using Image Processing,” International Journal of Advanced
Research in Science, Communication and Technology, vol. 6, no. 1, 2022.
[5] G. Iswarya, B. H. P and V. V. Reddy, “Sound Sensors to Control Traffic System for Emergency
Vehicles,” International Journal of Applied Engineering Research, vol. 13.
[6] H. Ghahremannezhad, H. Shi and C. Liu, “Real-Time Accident Detection in Traffic Surveillance
Using Deep Learning,” IEEE International Conference on Imaging Systems and Techniques (IST),
2022.
[7] M. Simoncic, “Road accidents in Slovenia involving a pedestrian, cyclist or motorcyclist and a car”.
[8] J. Wu, H. Xu, Y. Zhang and R. Sun, “An improved vehicle-pedestrian near-crash identification
method with a roadside LiDAR sensor,” Journal of Safety Research, vol. 73, 2020.
[9] R. Zhang, F. Li, J. Zhou and F. You, “A Review on Pedestrian Crossing Detection and Behavior
Analysis,” 2015.
[10] Y. Wang, S. Guo and H. Huang, “The Pedestrian Detecting and Counting System Based on
Automatic Method of CCD,” 9th International Conference on Advanced Infocomm Technology,
2017.
[11] D. N, D. K and A. Badage, “IoT based Automated Traffic Light Control System for Emergency
Vehicles using LoRa,” International Journal of Science Technology & Engineering, vol. 6, no. 1,
2019.
[12] P. V. B Raghuram Kadali 1 , “Modelling pedestrian road crossing behaviour under mixed traffic
condition,” 2013.
[13] A. H. Akoum, “Automatic Traffic Using Image Processing,” Journal of Software Engineering and
Applications, 2017.
[14] R. J. Franklin and Mohana, “Traffic Signal Violation Detection using Artificial Intelligence and
Deep Learning,” 2020.
[15] G. Pau, T. Campisi, A. Canale, T. Campisi, A. Severino, M. Collotta and G. Tesoriere, “Smart
Pedestrian Crossing Management at Traffic Light Junctions through a Fuzzy-Based Approach,”
2018.
[16] A. Makhmutova, R. Minnikhanov, M. Dagaeva, I. Anikin, T. Bolshakov and I. Khuziakhmetov,
“Intelligent Detection of Object's Anomalies for Road Surveilance Cameras,” 2019.
[17] U. E. Prakash, A. Thankappan, V. K. T. and A. A. Balakrishnan, Density Based Traffic Control
System Using Image Processing, 2018.
[18] M. Collotta, G. Pau, G. Scatà and T. Campisi, “A dynamic traffic light management system based
on wireless sensor networks for the reduction of the red-light running phenomenon.,” 2014.