This paper attempts to explore the possibility of using sound signatures for vehicle detection and
classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying
moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of
horns, random but identifiable back ground noises, continuous high energy noises on the back ground are
the different challenges encountered in the data collection. Different features were explored out of which
smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency
ceptral coefficients extracted from fixed regions around the detected peaks along with the
manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four
broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to
predict categories well.
CANNY EDGE DETECTION BASED REAL-TIME INTELLIGENT PARKING MANAGEMENT SYSTEMJANAK TRIVEDI
Real-time traffic monitoring and parking are very important aspects
for a better social and economic system. Python-based Intelligent Parking
Management System (IPMS) module using a USB camera and a canny edge
detection method was developed. The current situation of real-time parking slot
was simultaneously checked, both online and via a mobile application, with a
message of Parking “Available” or “Not available” for 10 parking slots. In
addition, at the time entering in parking module, gate open and at the time of exit
parking module, the gate closes automatically using servomotor and sensors.
Results are displayed in figures with the proposed method flow chart
MODEL BASED TECHNIQUE FOR VEHICLE TRACKING IN TRAFFIC VIDEO USING SPATIAL LOC...mlaij
In this paper, we proposed a novel method for visible vehicle tracking in traffic video sequence using model based strategy combined with spatial local features. Our tracking algorithm consists of two components: vehicle detection and vehicle tracking. In the detection step, we subtract the background and obtained candidate foreground objects represented as foreground mask. After obtaining foreground mask of
candidate objects, vehicles are detected using Co-HOG descriptor. In the tracking step, vehicle model is
constructed based on shape and texture features extracted from vehicle regions using Co-HOG and CSLBP method. After constructing the vehicle model, for the current frame, vehicle features are extracted from each vehicle region and then vehicle model is updated. Finally, vehicles are tracked based on the similarity measure between current frame vehicles and vehicle models. The proposed algorithm is evaluated based on precision, recall and VTA metrics obtained on GRAM-RTM dataset and i-Lids dataset. The experimental results demonstrate that our method achieves good accuracy.
International Journal of Computational Engineering Research (IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
P ERFORMANCE M EASUREMENTS OF F EATURE T RACKING AND H ISTOGRAM BASED T ...ijcsit
In this paper, feature tracking based and histogram
based traffic congestion detection systems are
developed. Developed all system are designed to run
as real time application. In this work, ORB (Orien
ted
FAST and Rotated BRIEF) feature extraction method h
ave been used to develop feature tracking based
traffic congestion solution. ORB is a rotation inva
riant, fast and resistant to noise method and conta
ins the
power of FAST and BRIEF feature extraction methods.
Also, two different approaches, which are standard
deviation and weighed average, have been applied to
find out the congestion information by using
histogram of the image to develop histogram based t
raffic congestion solution. Both systems have been
tested on different weather conditions such as clou
dy, sunny and rainy to provide various illumination
at
both daytime and night. For all developed systems p
erformance results are examined to show the
advantages and drawbacks of these systems.
CANNY EDGE DETECTION BASED REAL-TIME INTELLIGENT PARKING MANAGEMENT SYSTEMJANAK TRIVEDI
Real-time traffic monitoring and parking are very important aspects
for a better social and economic system. Python-based Intelligent Parking
Management System (IPMS) module using a USB camera and a canny edge
detection method was developed. The current situation of real-time parking slot
was simultaneously checked, both online and via a mobile application, with a
message of Parking “Available” or “Not available” for 10 parking slots. In
addition, at the time entering in parking module, gate open and at the time of exit
parking module, the gate closes automatically using servomotor and sensors.
Results are displayed in figures with the proposed method flow chart
MODEL BASED TECHNIQUE FOR VEHICLE TRACKING IN TRAFFIC VIDEO USING SPATIAL LOC...mlaij
In this paper, we proposed a novel method for visible vehicle tracking in traffic video sequence using model based strategy combined with spatial local features. Our tracking algorithm consists of two components: vehicle detection and vehicle tracking. In the detection step, we subtract the background and obtained candidate foreground objects represented as foreground mask. After obtaining foreground mask of
candidate objects, vehicles are detected using Co-HOG descriptor. In the tracking step, vehicle model is
constructed based on shape and texture features extracted from vehicle regions using Co-HOG and CSLBP method. After constructing the vehicle model, for the current frame, vehicle features are extracted from each vehicle region and then vehicle model is updated. Finally, vehicles are tracked based on the similarity measure between current frame vehicles and vehicle models. The proposed algorithm is evaluated based on precision, recall and VTA metrics obtained on GRAM-RTM dataset and i-Lids dataset. The experimental results demonstrate that our method achieves good accuracy.
International Journal of Computational Engineering Research (IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
P ERFORMANCE M EASUREMENTS OF F EATURE T RACKING AND H ISTOGRAM BASED T ...ijcsit
In this paper, feature tracking based and histogram
based traffic congestion detection systems are
developed. Developed all system are designed to run
as real time application. In this work, ORB (Orien
ted
FAST and Rotated BRIEF) feature extraction method h
ave been used to develop feature tracking based
traffic congestion solution. ORB is a rotation inva
riant, fast and resistant to noise method and conta
ins the
power of FAST and BRIEF feature extraction methods.
Also, two different approaches, which are standard
deviation and weighed average, have been applied to
find out the congestion information by using
histogram of the image to develop histogram based t
raffic congestion solution. Both systems have been
tested on different weather conditions such as clou
dy, sunny and rainy to provide various illumination
at
both daytime and night. For all developed systems p
erformance results are examined to show the
advantages and drawbacks of these systems.
Traffic Light Detection for Red Light Violation Systemijtsrd
The goal of Red Light Violation Detection System RLVDS is to track down vehicles that violated traffic regulations using surveillance cameras and image processing techniques. A complete automated red light runner detection algorithm that satisfies real time requirement and gives higher accuracy have been developed. The system detects simultaneously a traffic light sequence, stop line and detection region to detect moving vehicles and capture the vehicles beyond the stop line while the light is red and finally generates the red light running vehicle images with date and time information. In this paper, we propose a traffic light detection algorithm which is one of the processes of automated red light runner detection system. The proposed traffic light detection system includes four steps color space conversion based on RGB color space, some regions are extracted as candidates of a traffic light and morphological operation is applied to eliminate disturbance in the environment that can interfere with the traffic light states. Then, the types of traffic signals are judged by calculating image moments and the results of experiment show that the system is stable and reliable. Thwe War War Zaw | Ohnmar Win ""Traffic Light Detection for Red Light Violation System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25185.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25185/traffic-light-detection-for-red-light-violation-system/thwe-war-war-zaw
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
Control of traffic lights at the intersections of the main issues is the optimal traffic. Intersections to regulate traffic flow of vehicles and eliminate conflicting traffic flows are used. Modeling and simulation of traffic are widely used in industry. In fact, the modeling and simulation of an industrial system is studied before creating economically and when it is affordable. The aim of this article is a smart way to control traffic. The first stage of the project with the objective of collecting statistical data (cycle time of each of the intersection of the lights of vehicles is waiting for a red light) steps where the data collection found optimal amounts next it is. Introduced by genetic algorithm optimization of parameters is performed. GA begin with coding step as a binary variable (the range specified by the initial data set is obtained) will start with an initial population and then a new generation of genetic operators mutation and crossover and will Finally, the members of the optimal fitness values are selected as the solution set. The optimal output of Petri nets CPN TOOLS modeling and software have been implemented. The results indicate that the performance improvement project in intersections traffic control systems. It is known that other data collected and enforced intersections of evolutionary methods such as genetic algorithms to reduce the waiting time for traffic lights behind the red lights and to determine the appropriate cycle.
Automatic Road Sign Recognition From VideoDr Wei Liu
Road signs provide important information for guiding, warning, or regulating the drivers’ behaviour in order to make driving safer and easier. The Road Sign Recognition (RSR) is a field of applied computer vision research concerned with the automatic detection and classification of traffic signs in traffic scene images acquired from a moving car. Pavement Management Services has developed the first truly spatially registered video system in Australia. The digital video system offers continuous, high resolution video capture of five different views along the roadway. In this paper a road sign recognition system (RS2) for the high resolution roadside video recorded by PMS system will be introduced. The recognition process of RS2 is divided into three distinct parts: detection and location, recognition and classification, and display and record for information of road signs. While lots of attempts at automated sign recognition were based on the detection of shape patterns, the proposed method for PMS Video detects road signs by recognising their patterns in color space. Based on the performance testing of proposed RS2 for the road video collected in state highway network, the proposed approach is found to be robust and fast for detection of most of road signs commonly found in New Zealand, including warning signs, information signs, regulatory signs, and street signs. The sign recognition results include the exact locations of the road sign, types of road sign, and the images containing the road sign detected, which can be presented in various format and be used in sign condition evaluation for asset management.
Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
OpenCVand Matlab based Car Parking System Module for Smart City using Circle ...JANAK TRIVEDI
finding parking availability for a specific time period is
a very tedious job in urban areas. The Indian government now
focusing on t he smart city project, already they published city
name for a n upcoming smart city project. In smart city
application , intelligent transportation system (ITS) plays an
important role- in that finding parking place, specifically for the
car owner to avoid time computation, as well as congestion in
traffic is going to be very important. In this article, we propose
an intelligent car parking system for the smart city using Circle
Hough Transform (CHT).
Traffic Light Signal Parameters Optimization Using Modification of Multielement...IJECEIAES
A strategy to optimize traffic light signal parameters is presented for solving traffic con- gestion problem using modification of the Multielement Genetic Algorithm (MEGA). The aim of this method is to improve the lack of vehicle throughput (F ) of the works called as traffic light signal parameters optimization using the MEGA and Particle Swarm Optimization (PSO). In this case, the modification of MEGA is done by adding Hash-Table for saving some best populations for accelerating the recombination process of MEGA which is shortly called as H-MEGA. The experimental results show that the H-MEGA based optimization provides better performance than MEGA and PSO based methods (improving the F F F of both MEGA and PSO based optimization methods by about 10.01% (from 82,63% to 92.64%) and 6.88% (from 85.76% to 92.64%), respectively). In addition, the H-MEGA improve significantly the real F of Ooe Toroku road network of Kumamoto City, Japan about 21.62%.
The main objective of traffic surveillance system is to reduce the risk caused by accident. Many papers
published were concerned only about the vehicle detection during daytime. But we proposed a method to
detect the vehicle during night time. The main objective of our paper is to count the vehicles in night
time traffic scenes. It consists of three phases, they are vehicle’s headlight segmentation, tracking and
pairing. We know that in night time, only visible thing will be the headlight of the vehicle therefore we
are counting the vehicle based on the headlight information. Initially the headlights of a vehicle is
segmented based on the analysis of headlight size, location and area. Then tracked via a tracking
procedure designed to detect vehicle and then headlights are paired using the connected component
labeling. Based on the pairing result we can count the vehicles. Here we are using fuzzy hybrid
information inference mechanism for error compensation. Here by using fuzzy logic we can generate the
rules based on the size, color and position. In particular we are generating the rules based on the
distance between the headlights of a vehicle.Thereby we can improve the accuracy while counting the
vehicle.
VDIS: A System for Morphological Detection and Identification of Vehicles in ...rsmahabir
With the growth of urban centers worldwide, the number of vehicles in and around these areas has also increased. Traffic-related data plays an important role in spatial planning, for example, optimizing road networks and in the estimation or simulation of air and noise pollution. This information is important as it reflects the changes taking place around us. Additionally, data collected can be used for a wide array of applications including law enforcement, fleet management, and supporting other analyses at varying scales. In this paper, we present a method for the detection and identification of vehicles from low altitude, high spatial resolution Red Blue Green (RGB) images, utilizing both object spectra and image morphology. Results show an identification performance upwards of 62% with false positives occurring from the use of images with sun glare and vehicles with similar spectra values.
DYNAMIC TRAFFIC LIGHT CONTROL SCHEME FOR REDUCING CO2EMISSIONS EMPLOYING ETC ...ijmpict
With the increasing growth of vehicle numbers in the world, Global warming is becoming a serious issue. Vehicle CO2 emissions are considered to be one of the main sources of global warming. In order to reduce vehicles CO2 emissions, a dynamic traffic light control scheme is proposed. In the proposed scheme, we are the first to use Electronic Toll Collection (ETC) devices to obtain real time traffic flow information for a traffic control centre. By the proposed scheme, vehicles can pass through intersections with less waiting time and fewer numbers of stops. By smoothing vehicle travel, CO2 emissions can be reduced. Compared with fixed time control, the simulation results indicate that the proposed scheme has much better performance: vehicle average waiting time is greatly reduced and CO2 emissions can also be
reduced.
A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Inter...ijtsrd
In this paper, a two stage fuzzy logic system has been proposed to control an isolated intersection adaptively. The aim of this work is to minimize the average waiting time for a different traffic flow rates in real time means. In the first stage, the system consists of two modules named next phase selection module and the green phase extension module. In the second stage the system consists of the decision named module. The study was performed using SUMO traffic simulator. A comparison is made between a fuzzy logic controller and a conventional fixed time controller. As a result, fuzzy logic controller has shown better performance. Taha Mahmood | Muzamil Eltejani Mohammed Ali | Akif Durdu ""A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Intersection Based on Real Data using SUMO Simulator"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23873.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23873/a-two-stage-fuzzy-logic-adaptive-traffic-signal-control-for-an-isolated-intersection-based-on-real-data-using-sumo-simulator/taha-mahmood
Traffic Violation Detection Using Multiple Trajectories of VehiclesIJERA Editor
In general lane change violations are likely to happen before the stop line in the red-light violation detection
region. The system which can be detecting red-light and lane change violation is very useful for the traffic
management detection using vehicles moving in the region of interest and combining with the evaluation of the
trajectories behavior of multiple vehicles using mean square displacement (MSD) to detected both of violation.
We are using image processing technique only to detected traffic signal without help of another other system.
The experiment result shows that the algorithm is high accuracy to detect both of violation.
Development of Nighttime Visibility Assessment System for road using a Low Li...inventionjournals
Although the numbers of traffic accidents and fatalities in Korea have been decreased constantly, traffic accidents during night time have not been decreased. Thus, it is necessary to conduct comprehensive studies that can investigate, analyze, and assess the visibility environment of drivers in order to ensure safety in roads during nighttime. The purpose of this study is to develop the technology of acquiring and analyzing the nighttime driving environment in roads from driver's viewpoints. For this purpose, this study suggests a nighttime visibility assessment system that can quantify suitability. To do this, this study defined driver's visibility and selected effectiveness scale thereby developing an assessment model that reflected driver's level of recognition. The suggested system is developed consisting of two parts: the investigation device using a low light cameraequipped with investigation program and the web-based assessment program utilizing the document database. In the future, verification on the system will be conducted under various drivers’ visual environments and pilot field application will be planned to improve accuracy of assessment on nighttime road visibility based on the system.
In this study the controller for three tank multi loop system is designed using coefficient Diagram
method. Coefficient Diagram Method is one of the polynomial methods in control design. The
controller designed by using CDM technique is based on the coefficients of the characteristics
polynomial of the closed loop system according to the convenient performance such as equivalent
time constant, stability indices and stability limit. Controller is designed for the three tank process
by using CDM Techniques; the simulation results show that the proposed control strategies have
good set point tracking and better response capability.
A new design reuse approach for voip implementation into fpsocs and asicsijsc
The aim of this paper is to present a new design reuse approach for automatic generation of Voice over Internet protocol (VOIP) hardware description and implementation into FPSOCs and ASICs. Our motivation behind this work is justified by the following arguments: first, VOIP based System on chip (SOC) implementation is an emerging research and development area, where innovative applications can be implemented. Second, these systems are very complex and due to time to market pressure, there is a need to built platforms that help the designer to explore with different architectural possibilities and choose the circuit that best correspond to the specifications. Third, we aim to develop in hardware, design, methods and tools that are used in software like the MATLAB tool for VOIP implementation. To achieve our goal, the proposed design approach is based on a modular design of the VOIP architecture. The originality of our approach is the application of the design for reuse (DFR) and the design with reuse (DWR) concepts. To validate the approach, a case study of a SOC based on the OR1K processor is studied. We demonstrate that the proposed SoC architecture is reconfigurable, scalable and the final RTL code can be reused for any FPSOC or ASIC technology. As an example, Performances measures, in the VIRTEX-5 FPGA device family, and ASIC 65nm technology are shown through this paper.
Traffic Light Detection for Red Light Violation Systemijtsrd
The goal of Red Light Violation Detection System RLVDS is to track down vehicles that violated traffic regulations using surveillance cameras and image processing techniques. A complete automated red light runner detection algorithm that satisfies real time requirement and gives higher accuracy have been developed. The system detects simultaneously a traffic light sequence, stop line and detection region to detect moving vehicles and capture the vehicles beyond the stop line while the light is red and finally generates the red light running vehicle images with date and time information. In this paper, we propose a traffic light detection algorithm which is one of the processes of automated red light runner detection system. The proposed traffic light detection system includes four steps color space conversion based on RGB color space, some regions are extracted as candidates of a traffic light and morphological operation is applied to eliminate disturbance in the environment that can interfere with the traffic light states. Then, the types of traffic signals are judged by calculating image moments and the results of experiment show that the system is stable and reliable. Thwe War War Zaw | Ohnmar Win ""Traffic Light Detection for Red Light Violation System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25185.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25185/traffic-light-detection-for-red-light-violation-system/thwe-war-war-zaw
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
Control of traffic lights at the intersections of the main issues is the optimal traffic. Intersections to regulate traffic flow of vehicles and eliminate conflicting traffic flows are used. Modeling and simulation of traffic are widely used in industry. In fact, the modeling and simulation of an industrial system is studied before creating economically and when it is affordable. The aim of this article is a smart way to control traffic. The first stage of the project with the objective of collecting statistical data (cycle time of each of the intersection of the lights of vehicles is waiting for a red light) steps where the data collection found optimal amounts next it is. Introduced by genetic algorithm optimization of parameters is performed. GA begin with coding step as a binary variable (the range specified by the initial data set is obtained) will start with an initial population and then a new generation of genetic operators mutation and crossover and will Finally, the members of the optimal fitness values are selected as the solution set. The optimal output of Petri nets CPN TOOLS modeling and software have been implemented. The results indicate that the performance improvement project in intersections traffic control systems. It is known that other data collected and enforced intersections of evolutionary methods such as genetic algorithms to reduce the waiting time for traffic lights behind the red lights and to determine the appropriate cycle.
Automatic Road Sign Recognition From VideoDr Wei Liu
Road signs provide important information for guiding, warning, or regulating the drivers’ behaviour in order to make driving safer and easier. The Road Sign Recognition (RSR) is a field of applied computer vision research concerned with the automatic detection and classification of traffic signs in traffic scene images acquired from a moving car. Pavement Management Services has developed the first truly spatially registered video system in Australia. The digital video system offers continuous, high resolution video capture of five different views along the roadway. In this paper a road sign recognition system (RS2) for the high resolution roadside video recorded by PMS system will be introduced. The recognition process of RS2 is divided into three distinct parts: detection and location, recognition and classification, and display and record for information of road signs. While lots of attempts at automated sign recognition were based on the detection of shape patterns, the proposed method for PMS Video detects road signs by recognising their patterns in color space. Based on the performance testing of proposed RS2 for the road video collected in state highway network, the proposed approach is found to be robust and fast for detection of most of road signs commonly found in New Zealand, including warning signs, information signs, regulatory signs, and street signs. The sign recognition results include the exact locations of the road sign, types of road sign, and the images containing the road sign detected, which can be presented in various format and be used in sign condition evaluation for asset management.
Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
OpenCVand Matlab based Car Parking System Module for Smart City using Circle ...JANAK TRIVEDI
finding parking availability for a specific time period is
a very tedious job in urban areas. The Indian government now
focusing on t he smart city project, already they published city
name for a n upcoming smart city project. In smart city
application , intelligent transportation system (ITS) plays an
important role- in that finding parking place, specifically for the
car owner to avoid time computation, as well as congestion in
traffic is going to be very important. In this article, we propose
an intelligent car parking system for the smart city using Circle
Hough Transform (CHT).
Traffic Light Signal Parameters Optimization Using Modification of Multielement...IJECEIAES
A strategy to optimize traffic light signal parameters is presented for solving traffic con- gestion problem using modification of the Multielement Genetic Algorithm (MEGA). The aim of this method is to improve the lack of vehicle throughput (F ) of the works called as traffic light signal parameters optimization using the MEGA and Particle Swarm Optimization (PSO). In this case, the modification of MEGA is done by adding Hash-Table for saving some best populations for accelerating the recombination process of MEGA which is shortly called as H-MEGA. The experimental results show that the H-MEGA based optimization provides better performance than MEGA and PSO based methods (improving the F F F of both MEGA and PSO based optimization methods by about 10.01% (from 82,63% to 92.64%) and 6.88% (from 85.76% to 92.64%), respectively). In addition, the H-MEGA improve significantly the real F of Ooe Toroku road network of Kumamoto City, Japan about 21.62%.
The main objective of traffic surveillance system is to reduce the risk caused by accident. Many papers
published were concerned only about the vehicle detection during daytime. But we proposed a method to
detect the vehicle during night time. The main objective of our paper is to count the vehicles in night
time traffic scenes. It consists of three phases, they are vehicle’s headlight segmentation, tracking and
pairing. We know that in night time, only visible thing will be the headlight of the vehicle therefore we
are counting the vehicle based on the headlight information. Initially the headlights of a vehicle is
segmented based on the analysis of headlight size, location and area. Then tracked via a tracking
procedure designed to detect vehicle and then headlights are paired using the connected component
labeling. Based on the pairing result we can count the vehicles. Here we are using fuzzy hybrid
information inference mechanism for error compensation. Here by using fuzzy logic we can generate the
rules based on the size, color and position. In particular we are generating the rules based on the
distance between the headlights of a vehicle.Thereby we can improve the accuracy while counting the
vehicle.
VDIS: A System for Morphological Detection and Identification of Vehicles in ...rsmahabir
With the growth of urban centers worldwide, the number of vehicles in and around these areas has also increased. Traffic-related data plays an important role in spatial planning, for example, optimizing road networks and in the estimation or simulation of air and noise pollution. This information is important as it reflects the changes taking place around us. Additionally, data collected can be used for a wide array of applications including law enforcement, fleet management, and supporting other analyses at varying scales. In this paper, we present a method for the detection and identification of vehicles from low altitude, high spatial resolution Red Blue Green (RGB) images, utilizing both object spectra and image morphology. Results show an identification performance upwards of 62% with false positives occurring from the use of images with sun glare and vehicles with similar spectra values.
DYNAMIC TRAFFIC LIGHT CONTROL SCHEME FOR REDUCING CO2EMISSIONS EMPLOYING ETC ...ijmpict
With the increasing growth of vehicle numbers in the world, Global warming is becoming a serious issue. Vehicle CO2 emissions are considered to be one of the main sources of global warming. In order to reduce vehicles CO2 emissions, a dynamic traffic light control scheme is proposed. In the proposed scheme, we are the first to use Electronic Toll Collection (ETC) devices to obtain real time traffic flow information for a traffic control centre. By the proposed scheme, vehicles can pass through intersections with less waiting time and fewer numbers of stops. By smoothing vehicle travel, CO2 emissions can be reduced. Compared with fixed time control, the simulation results indicate that the proposed scheme has much better performance: vehicle average waiting time is greatly reduced and CO2 emissions can also be
reduced.
A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Inter...ijtsrd
In this paper, a two stage fuzzy logic system has been proposed to control an isolated intersection adaptively. The aim of this work is to minimize the average waiting time for a different traffic flow rates in real time means. In the first stage, the system consists of two modules named next phase selection module and the green phase extension module. In the second stage the system consists of the decision named module. The study was performed using SUMO traffic simulator. A comparison is made between a fuzzy logic controller and a conventional fixed time controller. As a result, fuzzy logic controller has shown better performance. Taha Mahmood | Muzamil Eltejani Mohammed Ali | Akif Durdu ""A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Intersection Based on Real Data using SUMO Simulator"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23873.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23873/a-two-stage-fuzzy-logic-adaptive-traffic-signal-control-for-an-isolated-intersection-based-on-real-data-using-sumo-simulator/taha-mahmood
Traffic Violation Detection Using Multiple Trajectories of VehiclesIJERA Editor
In general lane change violations are likely to happen before the stop line in the red-light violation detection
region. The system which can be detecting red-light and lane change violation is very useful for the traffic
management detection using vehicles moving in the region of interest and combining with the evaluation of the
trajectories behavior of multiple vehicles using mean square displacement (MSD) to detected both of violation.
We are using image processing technique only to detected traffic signal without help of another other system.
The experiment result shows that the algorithm is high accuracy to detect both of violation.
Development of Nighttime Visibility Assessment System for road using a Low Li...inventionjournals
Although the numbers of traffic accidents and fatalities in Korea have been decreased constantly, traffic accidents during night time have not been decreased. Thus, it is necessary to conduct comprehensive studies that can investigate, analyze, and assess the visibility environment of drivers in order to ensure safety in roads during nighttime. The purpose of this study is to develop the technology of acquiring and analyzing the nighttime driving environment in roads from driver's viewpoints. For this purpose, this study suggests a nighttime visibility assessment system that can quantify suitability. To do this, this study defined driver's visibility and selected effectiveness scale thereby developing an assessment model that reflected driver's level of recognition. The suggested system is developed consisting of two parts: the investigation device using a low light cameraequipped with investigation program and the web-based assessment program utilizing the document database. In the future, verification on the system will be conducted under various drivers’ visual environments and pilot field application will be planned to improve accuracy of assessment on nighttime road visibility based on the system.
In this study the controller for three tank multi loop system is designed using coefficient Diagram
method. Coefficient Diagram Method is one of the polynomial methods in control design. The
controller designed by using CDM technique is based on the coefficients of the characteristics
polynomial of the closed loop system according to the convenient performance such as equivalent
time constant, stability indices and stability limit. Controller is designed for the three tank process
by using CDM Techniques; the simulation results show that the proposed control strategies have
good set point tracking and better response capability.
A new design reuse approach for voip implementation into fpsocs and asicsijsc
The aim of this paper is to present a new design reuse approach for automatic generation of Voice over Internet protocol (VOIP) hardware description and implementation into FPSOCs and ASICs. Our motivation behind this work is justified by the following arguments: first, VOIP based System on chip (SOC) implementation is an emerging research and development area, where innovative applications can be implemented. Second, these systems are very complex and due to time to market pressure, there is a need to built platforms that help the designer to explore with different architectural possibilities and choose the circuit that best correspond to the specifications. Third, we aim to develop in hardware, design, methods and tools that are used in software like the MATLAB tool for VOIP implementation. To achieve our goal, the proposed design approach is based on a modular design of the VOIP architecture. The originality of our approach is the application of the design for reuse (DFR) and the design with reuse (DWR) concepts. To validate the approach, a case study of a SOC based on the OR1K processor is studied. We demonstrate that the proposed SoC architecture is reconfigurable, scalable and the final RTL code can be reused for any FPSOC or ASIC technology. As an example, Performances measures, in the VIRTEX-5 FPGA device family, and ASIC 65nm technology are shown through this paper.
An artificial neural network model for classification of epileptic seizures u...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper
the EEG signals are decomposed into a finite set of
bandlimited signals termed as Intrinsic mode functions.
The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd an
d 4th IMF's are used to extract features of epilepticsignal. A neural network using back propagation alg
orithm is implemented for classification of epilepsy.An overall accuracy of 99.8% is achieved in classification..
A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a
simple method for gait identification which is based on moments. Moment values are extracted on different
number of frames of Gray Scale and Silhouette images of CASIA database. These moment values are
considered as feature values. Fuzzy logic and Nearest Neighbor Classifier are used for classification. Both
achieved higher recognition.
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a
rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal
thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
The increasing popularity of animes makes it vulnerable to unwanted usages like copyright violations and
pornography. That’s why, we need to develop a method to detect and recognize animation characters. Skin
detection is one of the most important steps in this way. Though there are some methods to detect human
skin color, but those methods do not work properly for anime characters. Anime skin varies greatly from
human skin in color, texture, tone and in different kinds of lighting. They also vary greatly among
themselves. Moreover, many other things (for example leather, shirt, hair etc.), which are not skin, can
have color similar to skin. In this paper, we have proposed three methods that can identify an anime
character’s skin more successfully as compared with Kovac, Swift, Saleh and Osman methods, which are
primarily designed for human skin detection. Our methods are based on RGB values and their comparative
relations.
Solving the associated weakness of biogeography based optimization algorithmijsc
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and is based on an old theory of island biogeography that explains the geographical distribution of biological organisms. BBO was introduced in 2008 and then a lot of modifications and hybridizations were employed to enhance its performance. The researchers found that the original version of BBO has some weakness on its exploration. This paper tries to solve the root problems itself instead of solving its effect by using different techniques. It proposes two modifications; firstly, modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands. Secondly, the clear duplication process, which is located after the mutation stage, is sized to avoid any corruption on the suitability index variables of the non-mutated islands. The proposed modifications are extensively tested on 120 test functions with different dimensions and complexities. The results proved that the BBO performance can be enhanced effectively without embedding any additional sub-algorithm, and without using any complicated form of the immigration and emigration rates. In addition, the new BBO algorithm requires less CPU time and becomes even faster than the original simplified partial migration-based BBO. These essential modifications have to be considered as an initial step for any other modifications.
In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we
formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a
framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done
by comparing expert knowledge and system generated response. This basic emblematic approach using
fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and
provides support platform to pulmonary researchers in identifying the ailment effectively.
Recently, many studies are carried out with inspirations from ecological phenomena for developing
optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is
colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is
presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the
criteria to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour
of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve
continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV) is used to
convert the continuous position values to the discrete job sequences. The computational experiments show
that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is
able to find the optimal and best-known solutions on the instances studied.
An Artificial Neural Network Model for Classification of Epileptic Seizures U...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper the EEG signals are decomposed into a finite set of band
limited signals termed as Intrinsic mode functions. The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd and 4th IMF's are used to extract features of epileptic
signal. A neural network using back propagation algorithm is implemented for classification of epilepsy.
An overall accuracy of 99.8% is achieved in classification..
Broad phoneme classification using signal based featuresijsc
Speech is the most efficient and popular means of human communication Speech is produced as a sequence
of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The
state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC) features derived through short
time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme
classification is achieved using features derived directly from the speech at the signal level itself. Broad
phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified
useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR), short time
energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first
three formants. Features derived from short time frames of training speech are used to train a multilayer
feedforward neural network based classifier with manually marked class label as output and classification
accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction
which is useful for applications such as automatic speech recognition and automatic language
identification.
PERFUSION SYSTEM CONTROLLER STRATEGIES DURING AN ECMO SUPPORTijsc
In this work modelling and control of Perfusion system is presented. The Perfusion system simultaneously
controls the partial pressures during Extra Corporeal Membrane Oxygenation (ECMO) support. The
main Problem in ECMO system is exchange of Blood Gases in the Artificial Lung (Oxygenator).It is a
highly Nonlinear Process comprising time-varying parameters, and varying time delays, it is currently
being controlled manually by trained Perfusionist. The new control strategy implemented here has a
feedback linearization routine with time-delay compensation for the Partial pressuresof Oxygen and
Carbon dioxide. The controllers were tuned robustly and tested in simulations with a detailed artificial
Lung (Oxygenator) model in Cardiopulmonary bypass conditions. This Automatic control strategy is
proposed to improve the patient’s safety by fast control reference tracking and good disturbance rejection
under varying conditions.
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
HYBRID PARTICLE SWARM OPTIMIZATION FOR SOLVING MULTI-AREA ECONOMIC DISPATCH P...ijsc
We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system
environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation
to the power generators in such a manner that the total fuel cost is minimized while all operating
constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm
Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of
proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration
Coefficients (PSO-TVAC) and RCGA.
PRIVACY ENHANCEMENT OF NODE IN OPPORTUNISTIC NETWORK BY USING VIRTUAL-IDijsc
An entrepreneurial system is one of the sort of remote system. Delay resistance system is correspondence
organizing proposition which empowers the correspondence in such a situation where end to end way
might never be exist. Message is forward on the premise of chance. Time interim to convey a message is
long we can't evaluate or anticipate the time until we get the message. There is a security issue in these
sorts of system. In this paper we will proposed another procedure which will expand the protection of the
system and build execution of the system.
METAHEURISTIC OPTIMIZATION ALGORITHM FOR THE SYNCHRONIZATION OF CHAOTIC MOBIL...ijsc
We provide a scheme for the synchronization of two chaotic mobile robots when a mismatch between the
parameter values of the systems to be synchronized is present. We have shown how meta-heuristic
optimization can be used to adapt the parameters in two coupled systems such that the two systems are
synchronized, although their behavior is chaotic and they have started with different initial conditions and
parameter settings. The controlled system synchronizes its dynamics with the control signal in the periodic
as well as chaotic regimes. The method can be seen also as another way of controlling the chaotic
behavior of a coupled system. In the case of coupled chaotic systems, under the interaction between them,
their chaotic dynamics can be cooperatively self-organized. A synergistic approach to meta-heuristic
optimization search algorithm is developed. To avoid being trapped into local optimum and to enrich the
searching behavior, chaotic dynamics is incorporated into the proposed search algorithm. A chaotic Levy
flight is firstly incorporated in the proposed search algorithm for efficiently generating new solutions. And
secondly, chaotic sequence and a psychology factor of emotion are introduced for move acceptance in the
search algorithm. We illustrate the application of the algorithm by estimating the complete parameter
vector of a chaotic mobile robot.
Integrating vague association mining with markov modelijsc
The increasing demand of World Wide Web raises the need of predicting the user’s web page request. The
most widely used approach to predict the web pages is the pattern discovery process of Web usage mining.
This process involves inevitability of many techniques like Markov model, association rules and clustering.
Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov
models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague
set theory.
Classical methods for classification of pixels in multispectral images include supervised classifiers such as
the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector
machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a
method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin
2001 for classification and clustering. Random Forest grows many decision trees for classification. To
classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a
classification. The forest chooses the classification having the most votes. Random Forest provides a robust
algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing
multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral
images using various classifiers such as the maximum likelihood classifier, neural network, support vector
machine (SVM), and Random Forest and compare their results.
Fault diagnosis of a high voltage transmission line using waveform matching a...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Classification of Vehicles Based on Audio Signals using Quadratic Discriminan...ijsc
The focusof this paper is on classification of different vehicles using sound emanated from the vehicles. In this paper,quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor, and truck categories based on features such as short time energy, average zero cross rate, and pitch frequency of periodic segments of signals. Simulation results show that just by considering high energy feature vectors, better classification accuracy can be achieved due to the correspondence of low energy regions with noises of the background. To separate these elements, short time energy and average zero cross rate are used simultaneously.In our method, we have used a few features which are easy to be calculated in time domain and enable practical implementation of efficient classifier. Although, the computation complexity is low, the classification accuracy is comparable with other classification methods based on long feature vectors reported in literature for this problem.
Classification of vehicles based on audio signalsijsc
The focusof this paper is on classification of different vehicles using sound emanated from the vehicles. In
this paper,quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor, and
truck categories based on features such as short time energy, average zero cross rate, and pitch frequency of
periodic segments of signals. Simulation results show that just by considering high energy feature vectors,
better classification accuracy can be achieved due to the correspondence of low energy regions with noises
of the background. To separate these elements, short time energy and average zero cross rate are used
simultaneously.In our method,we have used a few features which are easy to be calculated in time domain
and enable practical implementation of efficient classifier. Although, the computation complexity is low,
the classification accuracy is comparable with other classification methodsbased on long feature vectors
reported in literature for this problem.
In the present scenario, research conducted is mostly based on determining the duration of green light.
Moreover the research papers published on Adaptive Traffic Management did not focus much on the
concept of handling Emergency Vehicles. This major role of this project is as a continuation to the
existing research papers published on this topic. Here we not only handle traffic effectively but also
elaborate on effective management of highly prioritized vehicles through all possible phases. In this
particular research paper, Wireless Sensor Networks (WSN) is assumed to be the source of input.
Traffic flow measurement for smart traffic light system designTELKOMNIKA JOURNAL
Determining congestions on intersection roads can significantly improve the performance of a traffic light system. One of the everyday problems on our roads nowadays is the unbalanced traffic on different roads. The blind view of roads and the dependency on the conventional timer-based traffic light systems can cause unnecessary delays on some arterial roads on expense of offering a needless extra pass time on some other secondary minor roads. In this paper, a foreground extraction model has been built in MATLAB platform to measure the congestions on the different roads constructing an intersection. Results show a satisfactory performance in terms of accuracy in counting cars and in consequence reducing the wait time on some major roads. System was tested under different weather and lighting conditions, and results were adequately promising.
Autonomous Traffic Signal Control using Decision Tree IJECEIAES
The objective of this paper is to introduce an effective and efficient way of traffic signal light control to optimize the traffic signal duration across each lanes and thereby, to minimize or completely eliminate traffic congestion. This paper introduces a new approach to resolve the traffic congestion problem at junctions by making use of decision trees. The vehicle count in the real time traffic video is determined by Image Processing technique. This information is fed to the decision tree based on which the decision is made regarding the status of traffic signal lights of each lane at the junction at any given instant of time.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
EXPLORING SOUND SIGNATURE FOR VEHICLE DETECTION AND CLASSIFICATION USING ANN
1. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
DOI: 10.5121/ijsc.2013.4203 29
EXPLORING SOUND SIGNATURE FOR VEHICLE
DETECTION AND CLASSIFICATION USING ANN
Jobin George1
, Anila Cyril2
, Bino I. Koshy3
and Leena Mary4
1
Department of EC Engineering, Rajiv Gandhi Institute of Technology, Kottayam, India
jobing4@gmail.com
2,3
Department of Civil Engineering, Rajiv Gandhi Institute of Technology, Kottayam,
India
anilacyril110@gmail.com, bino@rit.ac.in
4
Department of CS and Engineering, Rajiv Gandhi Institute of Technology, Kottayam,
India
leena.mary@rit.ac.in
ABSTRACT
This paper attempts to explore the possibility of using sound signatures for vehicle detection and
classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying
moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of
horns, random but identifiable back ground noises, continuous high energy noises on the back ground are
the different challenges encountered in the data collection. Different features were explored out of which
smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Mel-
frequency ceptral coefficients extracted from fixed regions around the detected peaks along with the
manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four
broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to
predict categories well.
KEYWORDS
Traffic characterises, vehicle detection, multilayer feedforward neural network, vehicle classification.
1. INTRODUCTION
Vehicles produce sound when they move. This include engine noise, noise due to tyre - pavement
interaction, body rattling, vibrations, horns etc. The sound level also depends on the
characteristics of the road and operating conditions. Under same type of operating conditions,
similar types of vehicle may emit similar sound, thus suggesting a specific sound signature for
each type of vehicle. Vehicle detection and classification have been attempted using many ways.
Image processing techniques are used to classify the vehicles under the real time traffic
management and for Intelligent Transportation Systems (ITS). Inductive loops based systems are
widely used for determination of vehicle counts.
Vehicle detection while it is in motion is a prerequisite for traffic and speed management,
classified vehicle count, traffic signal time optimization, gap/ headway measurement and military
purposes. Vehicle detection techniques are in a stage of continuous evolution. Traditional
methods use intrusive sensors such as inductive loops [1], magnetometers, microloop probes,
pneumatic road tubes and piezoelectric cables [2]. Unattended ground magnetometer sensor [3]
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30
was found to be effective to detect the vehicle passing. Since these intrusive methods decrease the
pavement life and affect traffic during installation and maintenance, it has been replaced by non
intrusive techniques such as video image processing, microwave radar, laser radar, passive
infrared, ultrasonic, passive acoustic array and combinations of sensor technologies such as
passive infrared and microwave doppler or passive infrared and ultrasonic [2].
Researches on the application of image processing techniques for vehicle detection have proved it
to be a perfect substitute for conventional methods. Now-a-days, video monitoring is widely used
for signal control and traffic management. Traffic video processed using unsupervised vehicle
detection and spatio-temporal tracking is effective in vehicle tracking in complex urban situations
[4]. In temporal difference based image processing (also known as Consecutive image subtraction
techniques), differences in consecutive image is analysed to detect a moving vehicle and its speed
[5]. Gray scale images that are frame differences of a video were effective for detecting vehicles.
Using edge detection, resolution reduction and modified morphological transformation techniques
when incorporated into processing techniques along with Hough transforms and fuzzy integrals, it
was possible to obtain a good vehicle detection system [6].
Processing videos for classification of vehicle detection and classification is computationally
intensive and time consuming. Vehicle classification using sound signal have been attempted by
researchers previously. Munich [7] compared conventional methods used for speaker recognition,
namely, systems based on Mel-frequency Cepstral Coefficients (MFCC) and either Gaussian
Mixture Models (GMM) or Hidden Markov Models (HMM), with Bayesian subspace method
based on the Short Term Fourier Transform (STFT) of the vehicles’ acoustic signature. He found
that a probabilistic subspace classifier outperformed conventional MFCC-GMM- and MFCC-
HMM-based systems by 50%. Two sets of features were fused for better vehicle classification
[8]. One being harmonic components, mainly characterizing engine noise and second is a group
of key frequency components, designated to reflect other minor but also important acoustic
factors, such as tire friction noise. Fusing these two sets of features provides a more complete
description of vehicles’ acoustic signatures, and reduces the limitation of relying one particular
feature set [8]. Bhave and Rao [9] reported an investigation of acoustic features relating to
vehicular traffic in relation to type of vehicle and state of motion in monitoring traffic congestion.
Different vehicles, broadly classified into two, three wheelers and heavy vehicle were studied for
their acoustic signatures. A source filter model of engine sound was used to derive suitable
features. The performance of formant based features is compared with that of Mel-Frequency
Cepstral Coefficients (MFCC) via a k-NN classifier on a manually labelled database of traffic
sounds.
In this work, we use smoothed logarithmic energy for automatically detecting vehicle by a peak
picking algorithm. Spectral characteristics of the sound signature represented using MFCC for
vehicles are used to train a classifier for three broad categories of vehicles. While testing, MFCC
extracted from automatically detected peaks are applied to the classifier to get the class labels.
Remaining part of the paper is organized as follows. Section 2 describes data collection procedure
and analysis of the collected data. In Section 3, methodology used for vehicle detection and
classification are discussed. Experimental details and results are discussed in Section 4. Section 5
summarizes the study in this paper.
2. DATA COLLECTION AND ANALYSIS
In this section the data collection and analysis is elaborated. Roadside recording was performed
on typical working days during several traffic conditions. The initial task pertained to sound
recordings without background noise to eliminate the need for filtering. Simultaneous video and
sound recordings were performed by Sony Handycam and Zoom H4n audio recorders. Stereo
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recordings using two onboard microphones were used for the study. The recordings were about
15-30 minutes continuous duration at a distance of 1.5m from pavement edge. The sites were
selected to capture the sound of moving vehicles without much acceleration or gear change. Also
the recordings were attempted without capturing undue attention from motorists on clear sunny
working days of the week.
2.1. Analysis of Vehicle Data
Collected vehicle data was analysed and studied using wavesurfer. Wavesurfer is a free software
tool designed for analysis and labelling of speech data. Here wavesurfer is used for labelling and
observing the characteristics of the recorded vehicle data as illustrated in Fig 1.
The vehicle sound recordings were manually labelled for identification of broad categories of
vehicles like light vehicles, medium vehicles and heavy vehicles as shown in Fig. 1(a). The light
vehicles included two and three wheelers. The medium type vehicles included cars, SUVs and
light trucks and the heavy vehicles included buses and trucks. Discernable incidents like horn
sound were also labelled. The noise of wind or accidental movement of audio recorder was not
labelled as an incident. Labelling was also done with the specific class of vehicles as hep, mep
and lp for heavy, medium, and light vehicles respectively as shown in Fig. 1(b). Horns were
labelled as hop.
Fig. 1. Visualisation of collected vehicle sound data using wavesurfer showing (a)& (b) broad
category labels (c) sound wave form (d) energy contour in dB (e) wideband spectrogram.
Analysing the energy contour shown in Fig. 1(d), it was observed that corresponding to passing of
each vehicle, there is an increase in energy. The maximum energy is recorded at the instant
vehicle crosses the audio recorder. The wideband spectrogram as in Fig. 1(e) shows presence of
high energy, spread at different range of the audio spectrum (0-20KHz). Spectrum corresponding
to horn shows a distinct periodicity in energy distribution as shown in Fig. 1(e). The analysis of
vehicle audio recording s indicated that detection of energy peaks may give an approximate count
of vehicles passing the recorder at a particular point.
(a)
(b)
(c)
(d)
(e)
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32
2.2. Features for Vehicle Detection
A preliminary study was conducted to identify discriminative features for vehicle detection.
Various features explored include the following: (i) short time energy (ii) log energy (iii)
smoothed log energy.
(i) Energy
One of the basic short-time analysis functions useful for vehicle sound signals is the
short-time energy. The short-time energy, En is defined as
∑∑ ==
−=−=
L
m
L
m
n mnwmxmnwmxE
1
22
1
2
][][])[][( (1)
Where L is the number of samples of the signal, w [n-m] represents a time shifted window
sequence, whose purpose is to select a segment of the sequence x[m] in the neighbourhoods of
sample m = n. As there are different forms for energy representation, we explored different ways
for calculating energy of the sound signal. Fig. 2 (a) and (b) show a sound waveform and
corresponding short time energy. It can be observed that the energy is relatively high for the
vehicle occurrence compared to the low energy regions corresponding to noises of the
background. But it consists of high frequency variations or ripples which may not be suited for
vehicle peak detection.
Fig. 2. Features explored for vehicle detection (a) sound waveform with labels (b) short time
energy (c) logarithmic energy in dB (d) smoothed logarithmic energy in dB
(ii) Logarithmic energy
In order to reduce high frequency variations and dynamic range of short term energy, the
logarithmic energy (in dB) is computed. As shown in Fig 2(c) log energy also contains high
frequency ripples/ components. Logarithmic energy in dB is found using 20*log(energy).
(iii) Smoothed logarithmic energy
Logarithmic energy in dB shown in Fig. 2(c) contains high frequency ripples/components which
may create problems while detecting the presence of vehicles using peak detection. Hence high
frequency components are removed using appropriately designed low pass filter. An elliptic low
pass filter with Rp = 3; Rs = 60; Wp = (1000/fs); Ws = (2000/fs) is used for this where fs is the
sampling rate (48000 samples/second), Rp is the pass band ripple factor, Rs is the stop band
ripple factor, Wp is the pass band frequency and Ws is the stop band frequency. Fig 2(d) shows
the smoothed logarithmic energy after removing the high frequency components from 2(c).
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3. METHODOLOGY FOR VEHICLE DETECTION AND CLASSIFICATION
3.1. Vehicle detection
The peak finding algorithm for vehicle detection is as follows.
1.Start with inspecting every 10 ms window of energy contour in dB, find peak for each
window, and inspect the values of neighboring samples (previous 40th
sample and next 40th
sample in our studies).
2.If both neighbouring point have very low value compared to the peak then identify the
considered peak as the potential peak, otherwise discard the peak.
3.Now move to next 10 ms of energy contour and repeat the procedure.
4.Plot the identified potential vehicle peaks.
Fig. 3. Methodology for vehicle detection (a) sound waveform (b) short time energy (c)
logarithmic energy in dB (d) smoothed logarithmic energy in dB with peaks marked (e) Detected
peaks corresponding to vehicles.
3.2. Algorithm for vehicle detection and classification
1. Divide 75% of the collected data for training and remaining for testing.
2. Do manual labelling for training and testing data as illustrated in Fig 1.
3. For each manually labelled peak compute 30 dimensional MFCC for 20ms region
around the peak. Perform ceptral mean subtraction for MFCC values. Normalise MFCC
value to the range between (0,1).
4. Initialise appropriate MLFFNN structure with random weights.
5. Train MLFFNN using MFCC as input and vehicle label as output using back
propagation training.
6. Testing of trained MLFFNN is done as shown in Fig 4. The various steps are:
i) Compute the log energy of test data
ii) Smooth the log energy using appropriate low pass filter.
iii) Detect peaks corresponding to vehicles using peak detection algorithm.
iv) Extract MFCC for the region around the detected peak.
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v) Perform cepstral mean subtraction to remove the influence of unwanted background
noise
vi) Normalize the MFCC values to limit its range between (0,1)
vii) Apply this normalized MFCC as input to trained MLFFNN and find out the output
labels.
viii) Compute the classification accuracy using the manual labels of test data
Fig 4. Block schematic illustrating various stages during the test phase
of vehicle detection and classification.
4. EXPERIMENTAL RESULTS
Vehicle sound recordings of about 120 minutes are used for demonstrating the effectiveness of
the proposed algorithm. Recording for 90 minutes duration is used for training and another
recording of duration 30 minutes is used for testing. In the proposed method, MFCC features are
used for training and testing purpose. Extracted features and labels along with is used for training
the MLFFNN with structure 13L 35N 10N 4N where L denotes neurons with linear activation
function, N denotes neurons with nonlinear (sigmoid in this case) activation function, and the
numerals denote the number of neurons in each layer. This MLFFNN classifier is then tested
using MFCC features derived from regions around automatically detected vehicle peaks in the
test data. The classification accuracy is computed with the manual labelling of test data. Results
are as shown in table 1. An overall classification of approximately 67% was obtained using this
method. Confusion matrix corresponding to test data 1 and 2 are given in Table 2 and 3
respectively, which indicate that maximum confusion has occurred in case of light and medium
category of vehicles which is obvious.
Table 1. Performance of proposed vehicle detection
and classification algorithm
Particulars of test data Vehicle classification accuracy (%)
Test data 1 67.4%
Test data 2 66%
Table 2. Confusion Matrix for Vehicle Classes (Data set- No.1)
Model
Test
Heavy Medium Light Horn
Heavy 7 1 2 0
Medium 3 16 11 2
Light 2 5 34 0
Horn 2 3 0 7
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Table 3. Confusion Matrix for Vehicle Classes (Data set- No.2)
Model
Test
Heavy Medium Light Horn
Heavy 5 1 2 0
Medium 1 14 4 3
Light 4 10 34 0
Horn 3 1 2 6
5. CONCLUSIONS
The present study has shown the potential of low pass filtered log energy being used for
automatic vehicle detection by locating peaks. The ANN classifier developed was able to predict
different categories of vehicles. The prediction success shows considerable confusion between the
classes of medium and light category of vehicles where similar types of engines are used. The
study has not addressed multilane traffic and its effect on the sound sampling due to simultaneous
arrivals of small vehicles, overtaking of different classes of vehicles. The effect of acceleration
and gear shift on classification need further exploration.
Acknowledgements
The authors would like to thank All India Council of Technical Education (AICTE), India for
supporting this study under the Research Promotion Scheme (RPS).
REFERENCES
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[6] Xiaobo Li, Zhi-Qiang Liu & Ka-Ming Leung, (2002) “Detection of vehicles from traffic scenes using
fuzzy integrals”, Pattern Recognition 35, pp 967–980.
[7] Mario Munich (2004) “Bayesian Subspace Methods for Acoustic Signature Recognition of Vehicles”,
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[8] Baofeng Guo, Mark Nixon & Thyagaraju Damrala, (2011) “Improving acoustic vehicle classification
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Authors
Jobin George graduated from Kannur University in Electronics and Communications
Engineering in 2010. He is currently doing masters degree in Advanced Communication
Engineering and Information Systems at Rajiv Gandhi Institute of Technology, Kottayam,
Kerala, India. His area of interest is signal processing and neural network
Anila Cyril graduated in Civil Engineering from College of Engineering, Trichur, Kerala
in 2011. She is currently perusing masters degree in Transportation Engineering at Rajiv
Gandhi Institute of Technology, Kottayam, Kerala, India. Her area of interest
is traffic simulation and modelling.
Bino I. Koshy received his Bachelors degree from Kerala University in 1985. He
obtained his M.Tech and Ph.D. from Indian Institute of Technology, Madras, India in
1987 and 2007 respectively. Currently he is working as Professor in Civil Engineering,
Rajiv Gandhi Institute of Technology, Kottayam, Kerala, India. His research interests are
travel demand modelling, traffic modelling and artificial neural networks. He is a life
member of Indian Society for Technical Education and Institute of Urban Transport
(India). He is also a Fellow of Institution of Engineers (India).
Leena Mary received her Bachelors degree from Mangalore University in 1988. She
obtained her M.Tech from Kerala University and Ph.D. from Indian Institute of
Technology, Madras, India. She has 22 years of teaching experience. Currently she is
working as Professor in Computer Science and Engineering at Rajiv Gandhi Institute of
Technology, Kottayam, Kerala, India. Her research interests are speech processing,
speaker forensics, signal processing and neural networks. She has published a book on
Extraction and Representation of Prosody for Speaker, Speech and Language Recognition
by Springer. She is a member of IEEE and a life member of Indian
Society for Technical Education.