Currently, smart city project is running in INDIA for
urban development. Under this project, intelligent transportation system (ITS) is the very significant step towards achieving the goal of reducing traffic congestion as well as different traffic monitoring applications, like – parking management, emergency vehicle detection, car speed detection, accidents detection, car
counting etc. To achieve intelligent transportation system’s goal
for traffic monitoring, Image and video processing becomes a
significant tool. In this article focus on vehicle counting, or say
car counting for available online video (YouTube) using -
Frame difference, Edge detection, Euclidean distance
methods, Morphology, adaptive threshold and effective
prediction of center position with addition of calculation of
change in positions, delta positions and Gaussian blur. To
differentiate car as an object with another object, we consider
here particular size for car objects or say four-wheeler
objects, which are different then pedestrians available on the
road, as well as different static objects –like a tree, posters
available on road etc. Here simulation results check for
Ahmedabad, Chennai, Bangalore, Mumbai traffic related
video available with different resolution on YouTube. Also
with night traffic conditions. For, Ahmedabad Traffic
video, simulation results validate using recall, precision, and
F1 parameter.
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACHJANAK TRIVEDI
We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic
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).
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
Real-time parking slot availability for Bhavnagar, using statistical block ma...JANAK TRIVEDI
Purpose-The purpose of this paper is to find a real-time parking location for a four-wheeler. Design/methodology/approach-Real-time parking availability using specific infrastructure requires a high cost of installation and maintenance cost, which is not affordable to all urban cities. The authors present statistical block matching algorithm (SBMA) for real-time parking management in small-town cities such as Bhavnagar using an in-built surveillance CCTV system, which is not installed for parking application. In particular, data from a camera situated in a mall was used to detect the parking status of some specific parking places using a region of interest (ROI). The method proposed computes the mean value of the pixels inside the ROI using blocks of different sizes (8 Â 10 and 20 Â 35), and the values were compared among different frames. When the difference between frames is more significant than a threshold, the process generates "no parking space for that place." Otherwise, the method yields "parking place available." Then, this information is used to print a bounding box on the parking places with the color green/red to show the availability of the parking place. Findings-The real-time feedback loop (car parking positions) helps the presented model and dynamically refines the parking strategy and parking position to the users. A whole-day experiment/validation is shown in this paper, where the evaluation of the method is performed using pattern recognition metrics for classification: precision, recall and F1 score. Originality/value-The authors found real-time parking availability for Himalaya Mall situated in Bhavnagar, Gujarat, for 18th June 2018 video using the SBMA method with accountable computational time for finding parking slots. The limitations of the presented method with future implementation are discussed at the end of this paper.
Classification and Detection of Vehicles using Deep Learningijtsrd
The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. The traditional methods of vehicle classification and detection are highly complex which provides coarse grained results due to suffering from limited viewpoints. Because of the latest achievements of Deep Learning, it was successfully applied to image classification and detection of objects. This paper presents a method based on a convolutional neural network, which consists of two steps vehicle classification and vehicle license plate recognition. Several typical neural network modules have been applied in training and testing the vehicle Classification and detection of license plate model, such as CNN convolutional neural networks , TensorFlow, Tesseract OCR. The proposed method can identify the vehicle type, number plate and other information accurately. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original training dataset and enriched testing dataset, the algorithm can obtain results with an average accuracy of about 97.32 in the classification and detection of vehicles. By increasing the amount of the data, the mean error and misclassification rate gradually decreases. So, this algorithm which is based on Deep Learning has good superiority and adaptability. When compared to the leading methods in the challenging Image datasets, our deep learning approach obtains highly competitive results. Finally, this paper proposes modern methods for the improvement of the algorithm and prospects the development direction of deep learning in the field of machine learning and artificial intelligence. Madde Pavan Kumar | Dr. K. Manivel | N. Jayanthi "Classification & Detection of Vehicles using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30353.pdf Paper Url :https://www.ijtsrd.com/engineering/software-engineering/30353/classification-and-detection-of-vehicles-using-deep-learning/madde-pavan-kumar
Automated License Plate Recognition for Toll Booth ApplicationIJERA Editor
This paper describes the Smart Vehicle Screening System, which can be installed into a tollbooth for automated recognition of vehicle license plate information using a photograph of a vehicle. An automated system could then be implemented to control the payment of fees, parking areas, highways, bridges or tunnels, etc. There are considered an approach to identify vehicle through recognizing of it license plate using image fusion, neural networks and threshold techniques as well as some experimental results to recognize the license plate successfully.
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.
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACHJANAK TRIVEDI
We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic
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).
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
Real-time parking slot availability for Bhavnagar, using statistical block ma...JANAK TRIVEDI
Purpose-The purpose of this paper is to find a real-time parking location for a four-wheeler. Design/methodology/approach-Real-time parking availability using specific infrastructure requires a high cost of installation and maintenance cost, which is not affordable to all urban cities. The authors present statistical block matching algorithm (SBMA) for real-time parking management in small-town cities such as Bhavnagar using an in-built surveillance CCTV system, which is not installed for parking application. In particular, data from a camera situated in a mall was used to detect the parking status of some specific parking places using a region of interest (ROI). The method proposed computes the mean value of the pixels inside the ROI using blocks of different sizes (8 Â 10 and 20 Â 35), and the values were compared among different frames. When the difference between frames is more significant than a threshold, the process generates "no parking space for that place." Otherwise, the method yields "parking place available." Then, this information is used to print a bounding box on the parking places with the color green/red to show the availability of the parking place. Findings-The real-time feedback loop (car parking positions) helps the presented model and dynamically refines the parking strategy and parking position to the users. A whole-day experiment/validation is shown in this paper, where the evaluation of the method is performed using pattern recognition metrics for classification: precision, recall and F1 score. Originality/value-The authors found real-time parking availability for Himalaya Mall situated in Bhavnagar, Gujarat, for 18th June 2018 video using the SBMA method with accountable computational time for finding parking slots. The limitations of the presented method with future implementation are discussed at the end of this paper.
Classification and Detection of Vehicles using Deep Learningijtsrd
The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. The traditional methods of vehicle classification and detection are highly complex which provides coarse grained results due to suffering from limited viewpoints. Because of the latest achievements of Deep Learning, it was successfully applied to image classification and detection of objects. This paper presents a method based on a convolutional neural network, which consists of two steps vehicle classification and vehicle license plate recognition. Several typical neural network modules have been applied in training and testing the vehicle Classification and detection of license plate model, such as CNN convolutional neural networks , TensorFlow, Tesseract OCR. The proposed method can identify the vehicle type, number plate and other information accurately. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original training dataset and enriched testing dataset, the algorithm can obtain results with an average accuracy of about 97.32 in the classification and detection of vehicles. By increasing the amount of the data, the mean error and misclassification rate gradually decreases. So, this algorithm which is based on Deep Learning has good superiority and adaptability. When compared to the leading methods in the challenging Image datasets, our deep learning approach obtains highly competitive results. Finally, this paper proposes modern methods for the improvement of the algorithm and prospects the development direction of deep learning in the field of machine learning and artificial intelligence. Madde Pavan Kumar | Dr. K. Manivel | N. Jayanthi "Classification & Detection of Vehicles using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30353.pdf Paper Url :https://www.ijtsrd.com/engineering/software-engineering/30353/classification-and-detection-of-vehicles-using-deep-learning/madde-pavan-kumar
Automated License Plate Recognition for Toll Booth ApplicationIJERA Editor
This paper describes the Smart Vehicle Screening System, which can be installed into a tollbooth for automated recognition of vehicle license plate information using a photograph of a vehicle. An automated system could then be implemented to control the payment of fees, parking areas, highways, bridges or tunnels, etc. There are considered an approach to identify vehicle through recognizing of it license plate using image fusion, neural networks and threshold techniques as well as some experimental results to recognize the license plate successfully.
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.
License Plate Recognition using Morphological Operation. Amitava Choudhury
This paper describes an efficient technique of locating and
extracting license plate and recognizing each segmented
character. The proposed model can be subdivided into four
parts- Digitization of image, Edge Detection, Separation of
characters and Template Matching. In this work, we propose a
method which is based on morphological operations where
different Structuring Elements (SE) are used to maximally
eliminate non-plate region and enhance plate region.
Character segmentation is done using Connected Component
Analysis. Correlation based template matching technique is
used for recognition of characters. This system is
implemented using MATLAB7.4.0. The proposed system is
mainly applicable to Indian License Plates.
Automatic face and VLP’S recognition for smart parking systemTELKOMNIKA JOURNAL
One of the concerning issues regarding smart city is Smart Parking. In Smart Parking, some
researchers try to provide solutions and breakthroughs on several research topics among security
systems, the availability of single space, an IoT framework, etc. In this study, we proposed a security
system on Smart Parking based on face recognition and VLP’s (Vehicle License Plates) identification. In
this research, SSIM (Structural Similarity) method as part of IQA has been applied due to its reliability and
simple computation for face detection and recognition process. From the test results of 30 data, obtained
the highest SSIM value 0.83 with the highest accuracy rate of 76.67%. That level of accuracy still has not
reached the implementation standard of 99.9%. So that it still needs to be improved in the future studies,
especially in the filtering noise section.
Enhancing Security and Privacy Issue in Airport by Biometric based Iris Recog...idescitation
Few years ago a self service has been predominant way of passenger at airport.
For the passenger that is a very enjoyable and comfort situation because it keeps control
over all process during their complete journey. For airport and for airlines is also very
interesting evolution because self service allows increasing capacity of airport without any
significant extra investment. However success of self service induces one potential risk. That
is of lack of human contact between airline operator and passenger, there is a problem in
identifying a passenger. This is definitely the problem for immigrations forcibly. This
potential risk of the industry is needed to be addressed and biometrics definitely can solve
this kind of problem. Nowadays biometric is considered to be the most important and
reliable method for personal identification. Iris recognition is considered as most personal
identification.
Design Approach for a Novel Traffic Sign Recognition System by Using LDA and ...IJERA Editor
This research paper highlights the problems that are encountered in a typical Traffic Sign Recognition System
like incorrect interpretation of a particular traffic sign which is observed by a driver while driving a vehicle
causing misunderstanding thereby resulting in road accidents. The visibility is affected by many environmental
factors such as smoke, rain, fog, humid weather, dust etc. and it is very difficult to understand the traffic signs in
this situations, causing misinterpretations of the particular traffic sign and resulting in road accidents. In order to
avoid this condition, a novel method of recognizing traffic signs is developed which take into consideration the
color and shape of the traffic sign. A algorithm called as Linear Discriminant Analysis (LDA) is used for
classification of different groups of traffic signs which are predefined by a particular set of features after the
process of Image Segmentation. The images are segmented by using the color and shape features of an image
and the features are extracted by using the Haar Transform and then the classification of images is done by using
Linear Discriminant Analysis Algorithm. Finally the GUI of traffic sign images is prepared by using the
software tool called as MATLAB.Our main objective is to recognize partially occluded traffic signs in a cloudy
environment by using LDA and to make an efficient Traffic Sign Detection system which will be capable of
recognizing and classifying any kind of known traffic sign from the other traffic signs by considering the color
and shape of the traffic sign on the basis of supervised classification of the training data so that any error which
results in a faulty detection or incorrect detection of traffic sign can be eliminated.
An Efficient Model to Identify A Vehicle by Recognizing the Alphanumeric Char...IJMTST Journal
Automatic Engine Number Recognition (AENR) is the digital image processing and an important aspect/role to identify the theft vehicles by recognizing characters, digits and special symbols. There is increase in the theft of vehicles, so to identify these theft vehicles, the proposed system is introduced. The proposed system controls the theft vehicles by recognizing a digits and characters in the number plate and chassis region and stores in the database in ASCII format to check the theft vehicles are registered or unregistered. Both system consists of 4 common phases: - Preprocessing, Character Extraction (ROI), Character Segmentation, and Character Recognition. This paper proposes a new scheme for engine number and chassis number extraction from the pre-processed image of the vehicle’s engine and chassis region using preprocess techniques, Region of Interest(ROI), Binarization, thresholding, template matching.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The automatic license plate recognition(alpr)eSAT Journals
Abstract Every country uses their own way of designing and allocating number plates to their country vehicles. This license number plate is then used by various government offices for their respective regular administrative task like- traffic police tracking the people who are violating the traffic rules, to identify the theft cars, in toll collection and parking allocation management etc. In India all motorized vehicle are assigned unique numbers. These numbers are assigned to the vehicles by district-level Regional Transport Office (RTO). In India the license plates must be kept in both front and back of the vehicle. These plates in general are easily readable by human due to their high level of intelligence on the contrary; it becomes an extremely difficult task for the computers to do the same. Many attributes like illumination, blur, background color, foreground color etc. will pose a problem. Index Terms: Automatic license plate recognition (ALPR) system, proposed methodology, reference
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.
License Plate Recognition using Morphological Operation. Amitava Choudhury
This paper describes an efficient technique of locating and
extracting license plate and recognizing each segmented
character. The proposed model can be subdivided into four
parts- Digitization of image, Edge Detection, Separation of
characters and Template Matching. In this work, we propose a
method which is based on morphological operations where
different Structuring Elements (SE) are used to maximally
eliminate non-plate region and enhance plate region.
Character segmentation is done using Connected Component
Analysis. Correlation based template matching technique is
used for recognition of characters. This system is
implemented using MATLAB7.4.0. The proposed system is
mainly applicable to Indian License Plates.
Automatic face and VLP’S recognition for smart parking systemTELKOMNIKA JOURNAL
One of the concerning issues regarding smart city is Smart Parking. In Smart Parking, some
researchers try to provide solutions and breakthroughs on several research topics among security
systems, the availability of single space, an IoT framework, etc. In this study, we proposed a security
system on Smart Parking based on face recognition and VLP’s (Vehicle License Plates) identification. In
this research, SSIM (Structural Similarity) method as part of IQA has been applied due to its reliability and
simple computation for face detection and recognition process. From the test results of 30 data, obtained
the highest SSIM value 0.83 with the highest accuracy rate of 76.67%. That level of accuracy still has not
reached the implementation standard of 99.9%. So that it still needs to be improved in the future studies,
especially in the filtering noise section.
Enhancing Security and Privacy Issue in Airport by Biometric based Iris Recog...idescitation
Few years ago a self service has been predominant way of passenger at airport.
For the passenger that is a very enjoyable and comfort situation because it keeps control
over all process during their complete journey. For airport and for airlines is also very
interesting evolution because self service allows increasing capacity of airport without any
significant extra investment. However success of self service induces one potential risk. That
is of lack of human contact between airline operator and passenger, there is a problem in
identifying a passenger. This is definitely the problem for immigrations forcibly. This
potential risk of the industry is needed to be addressed and biometrics definitely can solve
this kind of problem. Nowadays biometric is considered to be the most important and
reliable method for personal identification. Iris recognition is considered as most personal
identification.
Design Approach for a Novel Traffic Sign Recognition System by Using LDA and ...IJERA Editor
This research paper highlights the problems that are encountered in a typical Traffic Sign Recognition System
like incorrect interpretation of a particular traffic sign which is observed by a driver while driving a vehicle
causing misunderstanding thereby resulting in road accidents. The visibility is affected by many environmental
factors such as smoke, rain, fog, humid weather, dust etc. and it is very difficult to understand the traffic signs in
this situations, causing misinterpretations of the particular traffic sign and resulting in road accidents. In order to
avoid this condition, a novel method of recognizing traffic signs is developed which take into consideration the
color and shape of the traffic sign. A algorithm called as Linear Discriminant Analysis (LDA) is used for
classification of different groups of traffic signs which are predefined by a particular set of features after the
process of Image Segmentation. The images are segmented by using the color and shape features of an image
and the features are extracted by using the Haar Transform and then the classification of images is done by using
Linear Discriminant Analysis Algorithm. Finally the GUI of traffic sign images is prepared by using the
software tool called as MATLAB.Our main objective is to recognize partially occluded traffic signs in a cloudy
environment by using LDA and to make an efficient Traffic Sign Detection system which will be capable of
recognizing and classifying any kind of known traffic sign from the other traffic signs by considering the color
and shape of the traffic sign on the basis of supervised classification of the training data so that any error which
results in a faulty detection or incorrect detection of traffic sign can be eliminated.
An Efficient Model to Identify A Vehicle by Recognizing the Alphanumeric Char...IJMTST Journal
Automatic Engine Number Recognition (AENR) is the digital image processing and an important aspect/role to identify the theft vehicles by recognizing characters, digits and special symbols. There is increase in the theft of vehicles, so to identify these theft vehicles, the proposed system is introduced. The proposed system controls the theft vehicles by recognizing a digits and characters in the number plate and chassis region and stores in the database in ASCII format to check the theft vehicles are registered or unregistered. Both system consists of 4 common phases: - Preprocessing, Character Extraction (ROI), Character Segmentation, and Character Recognition. This paper proposes a new scheme for engine number and chassis number extraction from the pre-processed image of the vehicle’s engine and chassis region using preprocess techniques, Region of Interest(ROI), Binarization, thresholding, template matching.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The automatic license plate recognition(alpr)eSAT Journals
Abstract Every country uses their own way of designing and allocating number plates to their country vehicles. This license number plate is then used by various government offices for their respective regular administrative task like- traffic police tracking the people who are violating the traffic rules, to identify the theft cars, in toll collection and parking allocation management etc. In India all motorized vehicle are assigned unique numbers. These numbers are assigned to the vehicles by district-level Regional Transport Office (RTO). In India the license plates must be kept in both front and back of the vehicle. These plates in general are easily readable by human due to their high level of intelligence on the contrary; it becomes an extremely difficult task for the computers to do the same. Many attributes like illumination, blur, background color, foreground color etc. will pose a problem. Index Terms: Automatic license plate recognition (ALPR) system, proposed methodology, reference
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.
Machine vision based smart parking system using Internet of ThingsTELKOMNIKA JOURNAL
It is expected that in the next decade, majority of world population will be living in cities.
Better public services and infrastructures in the city are needed to cope with the booming population.
City vehicles that cruising for parking have indirectly causing traffic, making one harder to travel around the
city. Thus, a smart parking system can certainly lays the foundation to build a smart city. This paper
proposed a cost-effective IoT smart parking system to monitor city parking space and provide real-time
parking information to drivers. Moreover, instead of the conventional approach that uses embedded
sensors to detect vehicles in the parking area, camera image and machine vision technology are used to
obtain the parking status. In the prototype, twenty outdoor parking lots are covered using a 5 megapixel
camera connected to Raspberry Pi 3 installed at the 5th floor of the nearby building. Machine vision in this
project that involved motion tracking and Canny edge detection are programmed in Python 2 using
OpenCV technology. Corresponding data is uploaded to an IoT platform called Ubidots for possible
monitoring activity. An Android mobile application is designed for user to download real-time data of
parking information. This paper introduces a low cost smart parking system with the overall detection
accuracy of 96.40%. Also, the mobile application allows users to alert other car owners for any emergency
incidents and double parking blockage. The developed system can provide a platform for users to search
for empty car parking with ease and reduce the traffic issues such as illegal double parking especially in
the urban area.
Intelligent Parking Space Detection System Based on Image Segmentationijsrd.com
This paper aims to present an intelligent system for parking space detection based on image segmentation technique that capture and process the brown rounded image drawn at parking lot and produce the information of the empty car parking spaces. It will be display at the display unit that consists of seven segments in real time. The seven segments display shows the number of current available parking lots in the parking area. This proposed system, has been developed in software platform.
Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.
Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.
Smart Car Parking system using GSM Technologydbpublications
In this paper, we present PGS, a Parking Guidance System based on wireless sensor network(WSN) which guides a driver to an available parking lot. The system consists of a WSN based VDS (vehicle detection sub-system) and a management subsystem. The WSN based VDS gathers information on the availability of each parking lot and the management sub-system processes the information and refines them and guides the driver to the available parking lot by controlling a VMS (Variable Messaging System). The paper describes the overall system architecture of PGS from the hardware platform to the application software in the view point of a WSN. We implemented the WSN based VDS of PGS and experimented on the system with several kinds of cars.
Vehicle detection and tracking techniques a concise reviewsipij
Vehicle detection and tracking applications play an important role for civilian and military applications
such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection
process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic
analysis and vehicle categorizing objectives and may be implemented under different environments
changes. In this review, we present a concise overview of image processing methods and analysis tools
which used in building these previous mentioned applications that involved developing traffic surveillance
systems. More precisely and in contrast with other reviews, we classified the processing methods under
three categories for more clarification to explain the traffic systems.
In this paper, a project is described which is a 2-D
modelled version of a car that will learn how to drive itself. It
will have to figure everything out on its own. In addition, to
achieve that the simulator contains a car running
simultaneously &can be controlled by different control
algorithms - heuristic, reinforcement learning-based, etc. For
each dynamic input, the Reinforcement- Learning modifies
new patterns. Ultimately, Reinforcement Learning helps in
maximizing the reward from every state. In this first Part, we
will implement a Reinforcement-Learning model to build an
AI for Self Driving Car. Project will be focusing on the brain
of the car not any graphics. The car will detect obstacles and
take basic actions. To make autonomous car or self-driving
car a reality, some of the factors to be considered are human
safety and quality of life.
A computer vision-based lane detection approach for an autonomous vehicle usi...Md. Faishal Rahaman
Lane detection systems play a critical role in ensuring safe and secure driving by alerting the driver of lane departures. Lane detection may also save passengers' lives if they go off the road owing to driver distraction. The article presents a three-step approach for detecting lanes on high-speed video pictures in real-time and invariant lighting. The first phase involves doing appropriate prepossessing, such as noise reduction, RGB to grey-scale conversion, and binarizing the input picture. Then, a polygon area in front of the vehicle is picked as the zone of interest to accelerate processing. Finally, the edge detection technique is used to acquire the image's edges in the area of interest, and the Hough transform is used to identify lanes on both sides of the vehicle. The suggested approach was implemented using the IROADS database as a data source. The recommended method is effective in various daylight circumstances, including sunny, snowy, and rainy days, as well as inside tunnels. The proposed approach processes frame on average in 28 milliseconds and have a detection accuracy of 96.78 per cent, as shown by implementation results. This article aims to provide a simple technique for identifying road lines on high-speed video pictures utilizing the edge feature.
Neural Network based Vehicle Classification for Intelligent Traffic Controlijseajournal
Nowadays, number of vehicles has been increased and traditional systems of traffic controlling couldn’t be
able to meet the needs that cause to emergence of Intelligent Traffic Controlling Systems. They improve
controlling and urban management and increase confidence index in roads and highways. The goal of this
article is vehicles classification base on neural networks. In this research, it has been used a immovable
camera which is located in nearly close height of the road surface to detect and classify the vehicles. The
algorithm that used is included two general phases; at first, we are obtaining mobile vehicles in the traffic
situations by using some techniques included image processing and remove background of the images and
performing edge detection and morphology operations. In the second phase, vehicles near the camera are
selected and the specific features are processed and extracted. These features apply to the neural networks
as a vector so the outputs determine type of vehicle. This presented model is able to classify the vehicles in
three classes; heavy vehicles, light vehicles and motorcycles. Results demonstrate accuracy of the
algorithm and its highly functional level.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
2. So now look in the other fact that, if we know total no. of
four-wheelers, passed from the particular station, traffic
management becomes easier. Like total no. of Vehicles (four-
wheelers) entry in the particular area and total arrangement
for four-wheelers parking slot within that area compared and
accordingly advance booking system for a particular parking
slot in that area facilities to avoid parking management
problem and similarly to avoid congestion of the traffic for
that defined area. So, now our main focus is to count Four-
wheelers, particularly car for small-scale video, for developing
smart transportation, in a smart city.
IV. DIFFERENT METHODS DISCUSSED EARLIER
This type of work earlier established by different
authors, in [1] vehicle counting and classification, used
combinations of object detection, frame differentiation, edge
detection [12] and Kalman filtering [4] [11]. Whereas [2]
indicated this work with the simple principle of a light-beam
blocking system using the embedded system. Gaussian
Mixture Modelling based [8] [11] car counting system
explained in [3]. Lane marker detection for car counting
discussed in [4]. Model based object recognition system with
2 different approaches considered in [5]. Other methods are
using foreground extraction, object segmentation,
background subtraction and mathematical morphology [6][8]
[10] [11].
In many articles first step was the conversion of color to
grey, because many a time color information is not needed,
so this conversion step increase speed of the procedure [12].
Any computer vision research mainly or we can say mostly
depends upon light, variation in the light result may differ.
This content review in [7] i.e. traffic analysis at night time
using light intensity. 3D perspective mapping, use of 3D
coordinated from the captured video is explained in [9].
Template matching, full search block matching algorithm, k-
nearest neighbor methods, the neural network for
classification of different vehicle and various features based
vehicle detection, and active basis model discussed in [10].
Support vector machine based classification for traffic
explained [11].
One of the very interesting method [13] for counting
car using loci extraction method by linkage and separation,
detailed with all features consideration. Nearest object
distance and subtraction canny interpreted for automatic
counting of turtles [14].
IV. PROPOSED WORK
Proposed work-flow is shown in fig. [a], recorded
video (available video from YouTube) first captured, then
initialize car count equal to zero. For easy and speedy
calculation convert color information to grey information,
because in color, if consider only 3 basic color, though
computation task is complex compared to grey level
information. Define different scalar vector matrix for store
and indication of different value. Then in next step match the
current frame with an existing frame, or we can say compare
three consecutive frames one after another. Now the main
task is, first differentiates car object from other objects. For
that, different conditions are set before going to
implementation, (1) set rectangular size box (2) set aspect
ratio (3) set height and width (4) set threshold (5) create
contour and (6) convex hull.
In particular, this article, rectangular size shape,
predefined for car detection and car counting, though in some
videos rickshaws, trucks and other four-wheelers counted as a
car, which lies under false detection. For minimize false
detection in next step apply different morphology operation,
basically, dilation, erosion and convex hull and Gaussian blur
applied for removal of noise. Now for selected portion of
blob, area under which maximum four-wheelers, specifically
cars are present, shape and draw contour, using connected
component concepts.
Next step is, calculate distance between two points,
Euclidian distance measurement, equation for the same is,
D(a,b)=D(b,a)=
2 2 2 2 2 2
1 1 2 2
( ) (a ) .... (a )n n
a b b b
=
2
i i
1
(a b )
n
i
---------------------(1)
Where, D (b, a) is indicated Euclidian distance parameter an
and b indicates points for distance calculations. As per
equation (1) distance between n point measured using this
formula. Now frame by frame difference calculated, for edge
detection, using absolute differentiations, as indicated in
equation (2),
| A (xi, yi) − B (xi+ 1, yi+1) | ……………………… (2)
After this operation now set a different threshold using trial
and error method, for object differentiations, it may have
affected or changed accordingly, due to lighting condition as
well as video resolution clarity for a better result. Different
morphological operation, Erosion, dilation and convex hull.
Are used for object matching and car counting, with different
structure element – 3 x 3, 5 x 5, or 7 x 7.
From, Gonzalez and Woods, “Digital Image
Processing”, reference book, dilation operation ( ) on a
binary image, is to gradually enlarge the boundaries of regions
of foreground pixels [c], in equation (3) basic definitions of
dilation operation shown. Thus areas of foreground pixels
grow in size while holes within those regions become smaller.
And erosion operation, on a binary image, is to erode away the
boundaries of regions of foreground pixels. Thus areas of
foreground pixels shrink in size, and holes within those areas
become larger [c]. Mathematical representation of erosion
operation indicated in equation (4),
x ⊕ B = {p z2 | p= x + b, x∈ X, b B} ................. (3)
2
3. Sr.
No.
Set Threshold Total No. of Vehicle detected as
per discussed method
1 31 12
2 32 07
3 33 10
4 34 15
5 35 14
Here B, indicate structuring elements, X represent, elements of
given image, p is point available in Z structure.
X⊖B = {p z2 |∀ b B, p + b ∈ X} ……………. (4)
Proposed work-flow is shown in the fig. [a]
Basically, erosion operation shrinks the elements in a
given image, whereas due to dilation operation, image
elements or objects expand. Using convex hull, one of the
concepts from image morphology processing, contour created.
In present work, convex hulls and contour created using
different conditions with, define bounding rectangular area,
aspect ratio, width and height of selected blob. The Curving
out (convex) Hull is a subset of the N random points that form
the outer edge (border) to all other points.
For counting vehicle, reference line drawn in the
center of the image. Update and calculate, reference lines,
for car counting. For that, current center positions of x and y,
assigned with calculating at that time center positions of x and
y, and also calculating height and width for selected size
portion. For an update that positions, add delta values, for x
and y in the selected portion. Delta position indicates,
displacement of points from 0th
, position or say center
position to 1st
position, equation (5) and (6) represent delta
position for x and y position. Add this Delta position for next
displacement. Repeat this procedure until, covers all the
points, from selected bonding box or selected size box.
§ x (delta of x) = ∑ (xi –xi-1); ………… (5)
§ y (delta of y) = ∑ (yi –yi-1); …………. (6)
Add this § x, § y, for prediction of next positions.
Gaussian blur, is used for removing noise from the image,
and enhance image structures. Actually, in a Gaussian blur, a
convolution of a Gaussian function with image operation
done.
V. SIMULATION RESULT
Here simulation results collected with varying
threshold value, with the implementation of an above
discussed method simulation result on different traffic related
video, which is captured from Indian city – these listed cities
may become the smart city in near future. Our main focus to
develop a system for the smart city – we tried to apply these
on Chennai, Bangalore, Mumbai, Ahmedabad traffic video
using different threshold set. First Tabular result obtained for
Chennai traffic video –with Width X Height – 1280 x 720,
Frame rate -30, For total no. of Video Frame -90.
Chennai Traffic Video Result – Table 1.1
Here, Simulation result for the same, for threshold 34,
mentioned in fig. [1]
Fig. [1] Chennai traffic video [b] for 90 frames and
threshold value 34.
Tabular result obtained for Ahmedabad traffic video –with
Width X Height – 640 x 360, Frame rate -24, For total no. of
Video Frame -528
Ahmedabad Traffic Video Result – Table 1.2
Sr.
No.
Set
Threshold
correct Missed False
Positive
Recall Precision F1
1 31 8 5 0 0.61 1 0.75
2 32 13 0 0 1 1 1
3 33 13 0 0 1 1 1
4 34 11 2 0 0.84 1 0.88
5 35 13 0 3 1 0.81 0.89
3
4. Sr.
No.
Set Threshold Total No. of Vehicle
detected
1 31 57
2 32 62
3 33 65
4 34 65
5 35 52
Here, Simulation result for the same, for threshold 34,
mentioned in fig. [2]. While calculating car detection for the
second video, we have changed car detection – Horizontal Line
position calculation from rows to columns, due to change in
the recorded video orientation for getting a better result. For
measurement of these result, Recall, Precision and F1
parameter [d],
Recall = correct …………………. (7)
correct + missed
Precision is a ratio of correct detection to correct and false
positives detection. To find out overall performance of
algorithms that combines both recall and precision one most
important parameter used is F1 which is defined as follows
Precision = correct …………………. (8)
correct + false-positive
F1 = 2 x Recall x Precision …………. (9)
Recall + Precision
As here seen in table 1.2 compared to 1.1, the same result
repeated in case of threshold value – 32 and 33, which is an
exact matching of total car counting in given video. So for
Ahmedabad recorded video achieved 100 % results with
varying threshold and columns. In table 1.3 simulation result
shown in Bangalore traffic video.
Fig. [2] Ahmedabad traffic video [b] for 528 frames and
threshold value 34.
Bangalore traffic video – with Width X Height – 640 x 480,
Frame rate -30, For total no. of Video Frame -347.
Bangalore Traffic Video Result – Table 1.3
Fig. [3] Bangalore traffic video [b] for 347 frames and
threshold value 35.
Mumbai traffic video – with Width X Height – 1002 x 720,
Frame rate -25, For total no. of Video Frame -525.
Mumbai Traffic Video Result – Table 1.4
Sr.
No.
Set Threshold Total No. of Vehicle detected as
per
1 31 137
2 32 150
3 33 143
4 34 160
5 35 142
In Mumbai traffic video car counting in case of threshold
value 32 shown in figure [4].
Fig. [4] Mumbai traffic video [b] for 525 frames and threshold
value 32.
For, night traffic video (from YouTube), with different
illumination conditions, implement the same method, and we
get an almost same result as we received in daytime
conditions. Night traffic video, with Width X Height – 1280
X 720, frame rate -30, For total no. of 360 video frame. In
below, fig [5], vehicles counting with night conditions
mentioned.
4
5. In above 4 mentioned case, Mumbai, Bangalore and
Chennai traffic video with very congested traffic,
compared to Ahmedabad traffic video. For Ahmedabad
traffic video we have calculated Recall, Precision and F1
parameter for result verification.
Fig. [5] Car counting, in night traffic video, for 360 frames and
threshold value 35.
Night traffic Video Result – Table 1.5
Sr.
No.
Set Threshold Total No. of Vehicle detected as
per discussed method
1 31 12
2 32 12
3 33 12
4 34 12
5 35 12
Here, in all these tables, simulation results are
checked and car counting based on varying threshold value,
with applied proposed work discussed earlier. Tabular results,
show an important of a threshold value for the accurate
calculation of car counting. Now, in next table, simulation
results were shown, comparison, with use of dilation,
erosion operation, for constant threshold value. In table 1.6,
four cases compared, (1) With Erosion and dilation, (2) With
Dilation, Without erosion, (3) With Erosion, Without Dilation,
and (4) Without Dilation and Erosion.
In, mentioned all cases, dilation operation is firstly
applied, for expansion of the object, then once again apply
dilation and erosion operation for successful
implementation of vehicles counting procedure. So by
default dilation operation present, in the mentioned table 1.6,
all cases.
Comparative Analysis, with Dilation and Erosion effect –
Table 1.6
Sr.
No.
Video –
Frame rate
Morphology Effect No. of
Vehicle
counted
Approx.
range of
vehicle
counted
from
earlier
table.
1 Chennai
Traffic
Video -30
With Erosion and
dilation
10 ~7-15
With Dilation,
Without erosion
15
With Erosion,
Without Dilation,
11
Without Dilation
and Erosion
18
2 Ahmedabad
Traffic
Video-24
With Erosion and
dilation
13 ~8-13
With Dilation,
Without erosion
22
With Erosion,
Without Dilation,
17
Without Dilation
and Erosion
20
3 Bangalore
traffic
video -30
With Erosion and
dilation
65 ~52-65
With Dilation,
Without erosion
36
With Erosion,
Without Dilation,
29
Without Dilation
and Erosion
65
4 Mumbai
traffic
Video-25
With Erosion and
dilation
143 ~137-160
With Dilation,
Without erosion
117
With Erosion,
Without Dilation,
211
Without Dilation
and Erosion
173
5 Night
traffic
Video-30
With Erosion and
dilation
12 ~12
With Dilation,
Without erosion
9
With Erosion,
Without Dilation,
0
Without Dilation
and Erosion
10
VI. CONCLUSION AND FUTURE WORK
Here, five different types of video, collected from
YouTube–specially from one of the future smart cities, and
tried to implemented, proposed algorithm on it. In Chennai
video– video include, pedestrian, the big sign board of
advertisement, bus stand, and small vehicles, and etc.
Ahmedabad video, orientation is different from all other
videos, so in that case change rows to columns, a calculation
5
6. for effective car counting. In Bombay and Bangalore video,
congested traffic, consider for car counting calculation. And
in the last video, how this, proposed effective in different light
condition checked with Night traffic video.
From the result mentioned in the tabular result, for a
different city- which will become the smart city in nearer
future, car counting experiment conducted. For a different
value of the threshold, we get an almost accurate result. For
more congested traffic video, we have to set a threshold value
with trial and error method for an accurate result. For
Ahmedabad traffic video we have concluded with 100%
accuracy with the threshold value 32 and 33. That is shown
with Recall, Precision, and F1 parameter. For night video,
threshold values do not affect car counting results, which is
shown in table 1.5.
Limitations of these works, still it is not tested on
real time video, so image (video) taken from a different
camera, is not calculated. Second is sometimes rickshaw,
truck also counted as a car in car counter as a false detection.
Third limitations are not checked in each and every
illumination conditions.
In future work, our main focus is to overcome those
limitations, which are mentioned in this article. Next, we will
be trying to achieve automatic threshold value or double
threshold value for fast and accurate results. That will be
useful for developing Intelligent Transportation System,
which is one of the parts of Smart city project development.
ACKNOWLEDGMENT
In this article, Simulation result develops using
Visual Studio version 2015 and Open-cv Library and for
checking video Properties we used Matlab 2015 version.
REFERENCES
[1] A. Tourani and A. Shahbahrami, “Vehicle counting method
based on digital image processing algorithms,”2015 2nd Int.
Conf. Pattern Recognit. Image Anal., no. Ipria, pp. 1–6, 2015.
[2] M. Y. Chiu, R. Depommier, and T. Spindler, “An
embedded real-time vision system for 24-hour indoor/outdoor
car-counting applications,” Proc. - Int. Conf. Pattern
Recognit., vol. 3, pp. 338–341, 2004.
[3] J. Lu, Y. Xu, and X. Yang, “Counting pedestrians and cars
with an improved virtual gate method,” ICCASM 2010 -
2010 Int. Conf. Comput. Appl. Syst. Model. Proc., vol. 4,
no. Iccasm, pp. 448–452, 2010.
[4] L. Huang, “Real-time multi-vehicle detection and sub- feature
based tracking for traffic surveillance systems,”
CAR 2010 - 2010 2nd Int. Asia Conf. Informatics
Control. Autom. Robot., vol. 2, pp. 324–328, 2010.
[5] C. Setchell and E. L. Dagless, “Vision-based road- traffic
monitoring sensor,” IEE Proc. - Vision, Image, Signal
Process., vol. 148, no. 1, p. 78, 2001.
[6] I. K. E. Purnama, A. Zaini, B. N. Putra, and M. Hariadi,
“Real time vehicle counter system for intelligent
transportation system,” Int. Conf. Instrumentation, Commun.
Inf. Technol. Biomed. Eng. 2009, ICICI- BME 2009, pp. 4–
7, 2009.
[7] J. M. Mossi, A. Albiol, A. Albiol, and V. N. Ornedo, “Real-
time traffic analysis at night-time,” 2011 18th IEEE Int.
Conf. Image Process., pp. 2941–2944, 2011.
[8] A. J. Kun and Z. V??mossy, “Traffic monitoring with
computer vision,” SAMI 2009 - 7th Int. Symp. Appl. Mach.
Intell. Informatics, Proc., pp. 131–134, 2009.
[9] N. K. Kanhere and S. T. Birchfield, “Real-time
incremental segmentation and tracking of vehicles at low
camera angles using stable features,” IEEE Trans. Intell.
Transp. Syst., vol. 9, no. 1, pp. 148–159, 2008.
[10] S. Kamkar and R. Safabakhsh, “Vehicle detection,
counting and classification in various conditions,” IET Intell.
Transp. Syst., vol. 10, no. 6, pp. 406–413, 2016.
[11] Z. Chen, T. Ellis, and S. a Velastin, “Vehicle detection,
tracking and classification in urban traffic,” 15th Int. IEEE
Conf. Intell. Transp. Syst., pp. 951–956, 2012.
[12] S. Banerjee, P. Choudekar, and M. K. Muju, “Real time car
parking system using image processing,” ICECT 2011 -
2011 3rd Int. Conf. Electron. Comput. Technol., vol. 2, pp.
99–103, 2011.
[13] T. Hasegawa, K. Nohsoh, and S. Ozawa, “Counting cars
by tracking moving objects in the outdoor parking lot,” Proc.
VNIS’94 - 1994 Veh. Navig. Inf. Syst. Conf., pp. 63–68, 1994.
[14] J. J. Philipps, I. Bönninger, J. Vásquez, U. D. C. Rica, S.
José, and C. Rica, “Automatic Tracking and Counting
of Moving Objects,” pp. 93–97, 2014.
[15] https://github.com/
[16] https://www.youtube.com/
[17] Gonzalez and Woods , “Digital Image Processing”, 3rd
edition , Pearson https://chrisalbon.com/
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