{"id":117770,"date":"2026-05-18T13:02:26","date_gmt":"2026-05-18T07:32:26","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/?page_id=117770"},"modified":"2026-05-18T12:30:25","modified_gmt":"2026-05-18T07:00:25","slug":"a-research-study-on-traffic-monitoring-and-analysis-at-toll-plaza","status":"publish","type":"page","link":"https:\/\/www.mygreatlearning.com\/blog\/a-research-study-on-traffic-monitoring-and-analysis-at-toll-plaza\/","title":{"rendered":"A Research Study on Traffic Monitoring and Analysis At Toll Plaza"},"content":{"rendered":"\n<link rel=\"preconnect\" href=\"https:\/\/fonts.googleapis.com\">\n<link rel=\"preconnect\" href=\"https:\/\/fonts.gstatic.com\" crossorigin>\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=Inter:wght@400;500;600&family=Merriweather:ital,wght@0,300;0,400;0,700;1,300;1,400&family=Playfair+Display:wght@700;900&display=swap\" rel=\"stylesheet\">\n\n<style>\n    \/* SCOPING: All styles are prefixed with #custom-paper-scope *\/\n    #custom-paper-scope {\n        \/* CSS Variables for easy theming *\/\n        --font-heading: 'Playfair Display', serif;\n        --font-body: 'Merriweather', serif;\n        --font-ui: 'Inter', sans-serif;\n        \n        --color-paper: #ffffff;\n        --color-text-main: #2c3e50;\n        --color-text-light: #5f6c7b;\n        --color-accent: #34495e; 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\n        }\n        \n        #custom-paper-scope .breadcrumbs {\n            font-size: 0.75rem;\n        }\n        \n        #custom-paper-scope table {\n            display: block;\n            overflow-x: auto;\n        }\n    }\n<\/style>\n\n<div id=\"custom-paper-scope\">\n    <div class=\"paper-container\">\n        \n        <nav class=\"breadcrumbs\" aria-label=\"Breadcrumb\">\n            <a href=\"https:\/\/www.mygreatlearning.com\/\">Great Learning<\/a>\n            <span class=\"separator\">&gt;<\/span>\n            \n            <a href=\"https:\/\/www.mygreatlearning.com\/blog\/\">Blog<\/a>\n            <span class=\"separator\">&gt;<\/span>\n            \n            <a href=\"https:\/\/www.mygreatlearning.com\/blog\/research-and-studies\/\">Research and studies<\/a>\n            <span class=\"separator\">&gt;<\/span>\n            \n            <span class=\"current-crumb\">Traffic Monitoring and Analysis At Toll Plaza<\/span>\n        <\/nav>\n        \n        <h1 class=\"paper-title\" class=\"paper-title\" id=\"traffic-monitoring-and-analysis-at-toll-plaza\">Traffic Monitoring and Analysis At Toll Plaza<\/h1>\n        <div class=\"authors\">\n            <div class=\"author\">\n                <span class=\"author-name\">Narayana Darapaneni<\/span>\n                <span class=\"author-role\">Director-AIML<\/span>\n                <span class=\"author-affiliation\">Great Learning\/Northwestern University Illinois, USA<\/span>\n            <\/div>\n\n            <div class=\"author\">\n                <span class=\"author-name\">Parikshit Bangade<\/span>\n                <span class=\"author-role\">Student-AIML<\/span>\n                <span class=\"author-affiliation\">Great Learning<\/span>\n                <span class=\"author-affiliation\">Pune, India<\/span>\n            <\/div>\n\n            <div class=\"author\">\n                <span class=\"author-name\">Amit Mane<\/span>\n                <span class=\"author-role\">Student-AIML<\/span>\n                <span class=\"author-affiliation\">Great Learning<\/span>\n                <span class=\"author-affiliation\">Pune, India<\/span>\n            <\/div>\n\n            <div class=\"author\">\n                <span class=\"author-name\">Umang Maheshwari<\/span>\n                <span class=\"author-role\">Student-AIML<\/span>\n                <span class=\"author-affiliation\">Great Learning<\/span>\n                <span class=\"author-affiliation\">Pune, India<\/span>\n            <\/div>\n\n            <div class=\"author\">\n                <span class=\"author-name\">Sushilkumar C Thorawade<\/span>\n                <span class=\"author-role\">Student-AIML<\/span>\n                <span class=\"author-affiliation\">Great Learning<\/span>\n                <span class=\"author-affiliation\">Pune, India<\/span>\n            <\/div>\n\n            <div class=\"author\">\n                <span class=\"author-name\">Rushikesh Borse<\/span>\n                <span class=\"author-role\">Mentor-AIML<\/span>\n                <span class=\"author-affiliation\">Great Learning<\/span>\n                <span class=\"author-affiliation\">Pune, India<\/span>\n            <\/div>\n\n             <div class=\"author\">\n<span class=\"author-name\"><a href=\"https:\/\/scholar.google.com\/citations?user=T1KFBS0AAAAJ&hl=en&oi=ao\" target=\"_blank\">Anwesh Reddy Paduri<\/a><\/span>\n                <span class=\"author-role\">Data Scientist - AIML<\/span>\n                <span class=\"author-affiliation\">Great Learning<\/span>\n                <span class=\"author-affiliation\">Pune, India<\/span>\n            <\/div>\n        <\/div>\n\n        <div class=\"abstract\">\n            <div class=\"abstract-title\">Abstract<\/div>\n            <p>\n                This paper presents a solution to Traffic monitoring and analysis at Indian toll plazas. In some of the areas, the work is done on Vehicle detection and localization, vehicle registration detection, and character recognition and vehicle make classifier (Currently concentrated on Indian Cars only). Accurate detecting and localizing of objects in computer vision has always been a core problem, to the rescue of which Tensor Flow Object detection API comes with implementations provided by RCNN family (Basic RCNN, Fast RCNN, and Faster RCNN) and Single-shot detection models.\n            <\/p>\n            <p>\n                Experimenting over a range of models from Faster RCNN with inception v2, SSD MobileNet, and SSD inception v2 for vehicle and vehicle registration number detection problem we got higher accuracy with Faster RCNN with inception v2 for RMS Prop optimizer. For the detected vehicle registration number, we used an SVM-based multiclass classifier. A custom image dataset is used to train the model. The vehicle make classifier is implemented with VGG16 and the dataset was selected to contain only Indian cars.\n            <\/p>\n            <div class=\"keywords\">\n                <strong>Keywords:<\/strong> Vehicle Detection, Vehicle registration number, Faster RCNN, VGG16 and SVM.\n            <\/div>\n        <\/div>\n\n        <div class=\"section\">\n            <div class=\"section-title\">I. INTRODUCTION<\/div>\n            <p>\n                India has a huge web of highways all over the country connecting cities and states together. Road transportation being the most widely used mode of commuting and transporting goods from one city or state to other city and state. A need of monitoring and analysis of traffic at Indian roads is of paramount importance to civil and military surveillance for use of development and security-based decisions. Also, the derived insights could be a source of input to help find solutions to wide variety of problems a commuter face.\n            <\/p>\n            <p>\n                Indian traffic at toll plazas is very complex and critical with a diversified volume of vehicles likes of bikes, cars, trucks, and buses running on road at any given time of the day. Causes a wide variety problem of which congestion being most common on Indian toll plazas. The maintenance of these highways is very important to keep the connectivity intact. Toll Plazas or toll booths are special counters build on highways to collect tax from the general population using the highway for commuting. This tax is basically to retrieve the cost to build and maintain the highway. So, it a usual scenario of getting a huge congestion of traffic where everybody is lined up to pay the taxes before they move ahead. As a result of these congestion, it not only causes delays to commuters but also adds to loss of fuel and productive time. On a larger scale to observe the impact can be as large as impacting the economy negatively. Also, the kind of pollution caused in scenarios of traffic congestion is tremendous impacting environment.\n            <\/p>\n        <\/div>\n\n        <div class=\"section\">\n            <div class=\"section-title\">II. MOTIVATION OF WORK<\/div>\n            <p>\n                Spending hours at toll plaza in heavy traffic is very common in India and people do complain of waiting long before they are able to pay and pass the toll booth. As per the toll rules of NHAI [1] there is a max of 3 minutes waiting period for every vehicle to be able to pay and pass the toll booth and if this time exceeds beyond 3 minutes due to congestion, delay by toll booth authority or any other similar reasons should be allowed a free passage without a fee. A necessity of monitoring of the toll booth functioning and scenario identification of congestion help us find better solutions of several problems at toll booths.\n            <\/p>\n        <\/div>\n\n        <div class=\"section\">\n            <div class=\"section-title\">III. RELATED WORK<\/div>\n            <p>\n                Our proposed solution targeted few of the milestones on which past work done. A vision-based vehicle identification system providing solution of object extraction [16], object tracking, occlusion detection and segmentation, and vehicle classification. In situations where vehicles on the road may occlude each other, their trajectories may merge or split and to handle this they developed three processes: occlusion detection, motion vector calibration, and motion field clustering. Finally, the segmented objects were classified into seven different categorized vehicles [2].\n            <\/p>\n            <p>\n                Object Detection solution based on TensorFlow's Object Detection API being a promising technique providing ability to build and deploy image recognition software. Object detection technique not only classifies and recognizes objects of interest in images but also localizes them and marks them with bounding boxed around them. Solution mostly focuses on detecting harmful objects like threatening objects for which they got Tensor flow Object Detection API to train model and have used Faster R-CNN algorithm for implementation [3].\n            <\/p>\n            <p>\n                Region-based convolutional neural network for real-time hand gesture recognition. A Faster region-based convolutional neural network (Faster-RCNN) with Inception V2 architecture was used. Their solution observed average precision, average recall, and F1-score by training the model with a learning rate of 0.0002 for Adaptive Moment Estimation (ADAM) and Momentum optimizer, 0.004 for RMSprop optimizer. A better result of precision recall and F1-score values were attained with ADAM optimization algorithm after evaluating over custom test data [5].\n            <\/p>\n        <\/div>\n\n        <div class=\"section\">\n            <div class=\"section-title\">IV. OUR PROPOSED SYSTEM<\/div>\n            <p>\n                Proposed solution is an integration of models trained to help achieve objectives like vehicle detection on live video streams, Vehicle counting, Vehicle model type classification and vehicle registration number detection. This also involves distinguishing boundaries for the located objects into video frames and cropping the detected objects to be stored locally and used for further processing in outlaid milestones.\n            <\/p>\n            <p>\n                In Object Localization we detect where the object is situated, and Object detection involves detecting multiple objects in an image. In Object detection, we break down the original image into multiple images and perform Object Localization on each part. Extracted image parts are called ROI (Region of Interest) \/ Anchor Boxes\/Priors.\n            <\/p>\n            <p>\n                To solve the problem of classification and regression (localization), we have different models which uses different approaches such as R-CNN uses 'Selective Search' to extract ROI's. Fast-RCNN also uses Selective Search but extract ROIs from Feature Map. Faster R-CNN uses Region Proposal Network (RPN) to extract ROIs and in SSD, we (humans) provide the ROIs using multiple grids and anchor boxes (or priors). Allow CNN to look at different ROIs to see if there is an object in the ROI and what is their bounding box. Constructing a true Deep Learning model with ability to precisely localize and detect multiple objects in same image is always a core challenge with traditional computer vision and hence TensorFlow Object detection API an open-source solution built on top of TensorFlow makes it bit easy to construct, train and deploy Object detection models. So, for object detection we used TensorFlow Object detection API's one of the powerful tools to help us achieve the objective detection milestone and use for implementation.\n            <\/p>    \n<figure style=\"margin: 24px 0; text-align: center;\">\n  <img decoding=\"async\" \n    src=\"http:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2026\/05\/1.png\"\n    alt=\"Figure image\"\n    style=\"max-width:100%; height:auto;\"\n  >\n  <figcaption style=\"margin-top:8px; font-size:14px; color:#555;\">\n  <\/figcaption>\n<\/figure>\n            \n            <p>\n                The above figure shows the workflow followed in our solution. We started with detection and localization of our desired objects which are different types of vehicles and form.\n            <\/p>\n\n<figure style=\"margin: 24px 0; text-align: center;\">\n  <img decoding=\"async\" \n    src=\"http:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2026\/05\/2.png\"\n    alt=\"Figure image\"\n    style=\"max-width:100%; height:auto;\"\n  >\n  <figcaption style=\"margin-top:8px; font-size:14px; color:#555;\">\n  <\/figcaption>\n<\/figure>\n\n            <p>\n                Input image applied with a layers of convolution layers (with RELU activation function) Max pooling layer to concise the derived feature maps by extracting the max values of the feature map values to concentrate on the main features of detected objects. The output of pooling layer then flattened feature map then passed through fully connected Neural network layers. SoftMax activation enables to do multi-class classification.\n            <\/p>\n            <p>\n                For the detected vehicles in multiple video frames using Faster RCNN implementation it became a challenge to monitor the moving vehicles and hence we applied a naming logic to address the detected objects and monitor them in subsequent frames. A combination of location directory, a counter to track number of cropped images, class index id, class index name and object id were used as components to form a name for the detected vehicles. A unique object id talked about is derived via Centroid tracking algorithm are given in the derived name space which is then saved in csv format and finally merging all csv for detailed analysis, such as counting number of vehicles etc. Centroid tracking algorithm relies on the Euclidean distance between:\n            <\/p>\n            <ul>\n                <li>Existing object centroids (i.e., objects the centroid tracker has already seen before).<\/li>\n                <li>New object centroids between subsequent frames in a video.<\/li>\n            <\/ul>\n            <p>\n                Further to the processing in the solution the detected vehicles with bounding boxes are cropped and used for vehicle make classification and vehicle registration number detection. K. Simonyan and A. Zisserman [4] from the University of Oxford in the paper \"Very Deep Convolutional Networks for Large-Scale Image Recognition\" proposed VGG16 convolutional neural network. We used VGG16 based image classifier for identifying the make of detected vehicles in first stage of our system.\n            <\/p>\n\n<figure style=\"margin: 24px 0; text-align: center;\">\n  <img decoding=\"async\" \n    src=\"http:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2026\/05\/3.png\"\n    alt=\"Figure image\"\n    style=\"max-width:100%; height:auto;\"\n  >\n  <figcaption style=\"margin-top:8px; font-size:14px; color:#555;\">\n  <\/figcaption>\n<\/figure>\n            <p>\n                Finally, the output of all these models is clubbed to form a CSV file with insights like Vehicle counts, Vehicle registration number, Vehicle make classification description along with timestamp showing the date and time. This insight then redirected over dashboard for live monitoring and further analysis. With all the data collected via the above workflow can be conducted to help in different domains of interest:\n            <\/p>\n            <ul>\n                <li>Traffic inflow at toll plazas to extract busiest day, week of the month, busies hour of the day etc.<\/li>\n                <li>Toll collections stats and each vehicle service time monitoring.<\/li>\n                <li>Security insights with Vehicle registration number detection and vehicle model type detection.<\/li>\n            <\/ul>\n        <\/div>\n\n        <div class=\"section\">\n            <div class=\"section-title\">V. ALGORITHMS<\/div>\n            \n            <div class=\"subsection-title\">A. SVM based Character Recognition<\/div>\n            <p>The hypothesis function h is defined as:<\/p>\n            <p>\n                h(x<sub>i<\/sub>) = { +1 if wx+b &ge; 0; -1 if wx+b &lt; 0 } &nbsp; &nbsp; (1)\n            <\/p>\n            <p>\n                The point above or on the hyperplane will be classified as class +1, and the point below the hyperplane will be classified as class 1. Computing the (soft margin) SVM classifier amounts to minimizing an expression of the form,\n            <\/p>\n            <p>\n                [ (1\/n) &Sigma;<sub>i=1<\/sub><sup>n<\/sup> max(0, 1 - y<sub>i<\/sub>(w.x<sub>i<\/sub> - b)) ] + &lambda;||w||<sup>2<\/sup> &nbsp; &nbsp; (2)\n            <\/p>\n            <p>\n                We focus on the soft-margin classifier since choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data.\n            <\/p>\n            <p>\n                The same cropped image of object detection stage of our system was used as an input for vehicle registration number detection. A background subtraction achieved with Otsu Thresholding. Background subtracted Images were then subjected to segmentation where each character was further predicted with an SVM based character recognition algorithm.\n            <\/p>\n\n<figure style=\"margin: 24px 0; text-align: center;\">\n  <img decoding=\"async\" \n    src=\"http:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2026\/05\/4.png\"\n    alt=\"Figure image\"\n    style=\"max-width:100%; height:auto;\"\n  >\n  <figcaption style=\"margin-top:8px; font-size:14px; color:#555;\">\n  <\/figcaption>\n<\/figure>\n\n            <div class=\"subsection-title\">B. Faster R-CNN<\/div>\n            <p>\n                Replaces the selective search method with region proposal network (RPN) which makes the algorithm faster time\/Image - 0.2 second.\n            <\/p>\n            <p>\n                IoU = (Anchor &cap; GroundTruth) \/ (Anchor &cup; GroundTruth) &nbsp; &nbsp; (3)\n            <\/p>\n            <p>\n                IoU = (A &cap; Gt) \/ (A &cup; Gt) { &gt; 0.5 = Object; &lt; 0.5 = No Object } &nbsp; &nbsp; (4)\n            <\/p>\n            <p>\n                Computing the Intersection over Union is dividing the area of overlap between the bounding boxes by the area of union. In the numerator we compute the area of overlap between the predicted bounding box and the ground-truth bounding box. We are keeping the threshold value of 0.5. The denominator is the area of union, or more simply, the area encompassed by both the predicted bounding box and the ground-truth bounding box. Dividing the area of overlap by the area of union yields our final score the Intersection over Union.\n            <\/p>\n        <\/div>\n\n        <div class=\"section\">\n            <div class=\"section-title\">VI. SYSTEM ENVIRONMENT SETUP<\/div>\n            \n            <div class=\"subsection-title\">A. Hardware<\/div>\n            <table>\n                <caption><strong>TABLE 1: HARDWARE COMPARISON<\/strong><\/caption>\n                <thead>\n                    <tr>\n                        <th>Machine Type<\/th>\n                        <th>CPU Processor<\/th>\n                        <th>GPU<\/th>\n                        <th>RAM<\/th>\n                        <th>Clock speed<\/th>\n                    <\/tr>\n                <\/thead>\n                <tbody>\n                    <tr>\n                        <td>Machine 1 Asus vivo book-max A541uv<\/td>\n                        <td>Intel\u00ae Core TM i3 7100U<\/td>\n                        <td>Nvidia 920MX<\/td>\n                        <td>12GB<\/td>\n                        <td>2.4GHz<\/td>\n                    <\/tr>\n                    <tr>\n                        <td>Machine 2 Acer Predator Helios 300<\/td>\n                        <td>Intel\u00ae Hexacore Intel I7 9750<\/td>\n                        <td>Nvidia RTX 2060<\/td>\n                        <td>16GB<\/td>\n                        <td>2.6Ghz<\/td>\n                    <\/tr>\n                <\/tbody>\n            <\/table>\n\n            <div class=\"subsection-title\">B. Dataset<\/div>\n            <table>\n                <caption><strong>TABLE 2: DATASET DETAILS<\/strong><\/caption>\n                <thead>\n                    <tr>\n                        <th>Models<\/th>\n                        <th>Dataset<\/th>\n                        <th>Train Images<\/th>\n                        <th>Testing Images<\/th>\n                        <th>Number of classes<\/th>\n                        <th>images \/classes<\/th>\n                    <\/tr>\n                <\/thead>\n                <tbody>\n                    <tr>\n                        <td>Vehicle detection Model<\/td>\n                        <td>Google image dataset v4, Custom images<\/td>\n                        <td>701<\/td>\n                        <td>180<\/td>\n                        <td>6<\/td>\n                        <td>145 approx.<\/td>\n                    <\/tr>\n                    <tr>\n                        <td>Vehicle registration number detection model<\/td>\n                        <td>Google image dataset v4, Custom images<\/td>\n                        <td>2000<\/td>\n                        <td>376<\/td>\n                        <td>1<\/td>\n                        <td>2376<\/td>\n                    <\/tr>\n                    <tr>\n                        <td>Vehicle Make classifier<\/td>\n                        <td>Google Custom images using labelImg<\/td>\n                        <td>689<\/td>\n                        <td>272<\/td>\n                        <td>17<\/td>\n                        <td>60 approx.<\/td>\n                    <\/tr>\n                    <tr>\n                        <td>SVM Character recognition (0-9 digits, 26 alphabets)<\/td>\n                        <td>Google Custom images using labelImg<\/td>\n                        <td>1260<\/td>\n                        <td>180<\/td>\n                        <td>36<\/td>\n                        <td>40 per character<\/td>\n                    <\/tr>\n                <\/tbody>\n            <\/table>\n\n            <p>\n                These images for object detection were taken to ensure that they have the desired vehicle class covering major portion of the image with a wide variety of vehicle orientation like middle\/close range in front of the camera, middle\/close range in the left, close\/middle range in the right, and far range to enable the model to learn better. The images sizes vary from 300*300 to 1000*1200 pixels. Several instances of a same vehicle are included with different bounding hypotheses. Also, few images of multiple classes like Car, Bus or Truck and person was included to enable the model to learn various weights from such perspective. We included around 120-150 images per class and the classes were Car, Truck, Bus, Rickshaw, bike and Person.\n            <\/p>\n\n<figure style=\"margin: 24px 0; text-align: center;\">\n  <img decoding=\"async\" \n    src=\"http:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2026\/05\/5.png\"\n    alt=\"Figure image\"\n    style=\"max-width:100%; height:auto;\"\n  >\n  <figcaption style=\"margin-top:8px; font-size:14px; color:#555;\">\n  <\/figcaption>\n<\/figure>\n            <p>\n                For vehicle registration detection we used images with vehicle registration numbers covering the entire image size. To add diversity, we included Indian plus foreign registration number plates and multiline number plates to enable our model to learn them better. The selected images were of size 20*20 pixels for SVM based character recognition model.\n            <\/p>\n\n<figure style=\"margin: 24px 0; text-align: center;\">\n  <img decoding=\"async\" \n    src=\"http:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2026\/05\/6.png\"\n    alt=\"Figure image\"\n    style=\"max-width:100%; height:auto;\"\n  >\n  <figcaption style=\"margin-top:8px; font-size:14px; color:#555;\">\n  <\/figcaption>\n<\/figure>\n\n            <p>\n                At this moment our solution only considers the class Cars for Vehicle make classification for which we used google images of Indian car models like Honda Amaze, Hyundai i10, Suzuki Wagon R, Suzuki Swift etc. Images ranging from 300*300 to 1000*1200 pixels majorly coving the entire image size. The Cars were selected to have wide variety of vehicle orientation like middle\/close range in front of the camera, middle\/close range in the left, close\/middle range in the right, and far range to enable the model learn better.\n            <\/p>\n        <\/div>\n\n        <div class=\"section\">\n            <div class=\"section-title\">VII. RESULTS & DISCUSSION<\/div>\n            \n            <div class=\"subsection-title\">A. False detection results<\/div>\n            <p>\n                For vehicle registration detection the model detected the flashlights as vehicle registration number.\n            <\/p>\n\n<figure style=\"margin: 24px 0; text-align: center;\">\n  <img decoding=\"async\" \n    src=\"http:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2026\/05\/7.png\"\n    alt=\"Figure image\"\n    style=\"max-width:100%; height:auto;\"\n  >\n  <figcaption style=\"margin-top:8px; font-size:14px; color:#555;\">\n  <\/figcaption>\n<\/figure>\n\n            <div class=\"subsection-title\">B. Results of Evaluation<\/div>\n            <p>\n                For object detection we use the concept of Intersection over Union (IoU). IoU a Jaccard index-based computes intersection over the union of the two bounding boxes; the bounding box for the ground truth and the predicted bounding box. So, a value of 1 would be perfect overlap.\n            <\/p>\n\n<figure style=\"margin: 24px 0; text-align: center;\">\n  <img decoding=\"async\" \n    src=\"http:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2026\/05\/8.png\"\n    alt=\"Figure image\"\n    style=\"max-width:100%; height:auto;\"\n  >\n  <figcaption style=\"margin-top:8px; font-size:14px; color:#555;\">\n  <\/figcaption>\n<\/figure>\n\n            <p>\n                With a threshold value the prediction can be expressed as positive or negative. Let us say IoU threshold is set to 0.5, in that case:\n            <\/p>\n            <ul>\n                <li>if IoU &ge; 0.5 classify the object detection as True Positive (TP).<\/li>\n                <li>if IoU &lt; 0.5, then it is a wrong detection and classify it as False Positive (FP).<\/li>\n                <li>When a ground truth is present in the image and model failed to detect the object, classify it as False Negative (FN).<\/li>\n                <li>True Negative (TN): TN is every part of the image where we did not predict an object. This metrics is not useful for object detection, hence we ignore TN.<\/li>\n            <\/ul>\n            <p>\n                Set IoU threshold value to 0.5 or greater. It can be set to 0.5, 0.75, 0.9 or 0.95 etc.\n            <\/p>\n\n            <div class=\"subsection-title\">C. Losses<\/div>\n            <p>\n                L({pi},{ti}) = (1\/N<sub>cls<\/sub>)&Sigma;<sub>i<\/sub>L<sub>cls<\/sub>(p<sub>i<\/sub>,p<sub>i<\/sub><sup>*<\/sup>) + &lambda;(1\/N<sub>reg<\/sub>)&Sigma;<sub>i<\/sub>p<sub>i<\/sub><sup>*<\/sup>L<sub>reg<\/sub>(t<sub>i<\/sub>,t<sub>i<\/sub><sup>*<\/sup>) &nbsp; &nbsp; (5)\n            <\/p>\n            <ul>\n                <li>i = index of an anchor box in an mini batch<\/li>\n                <li>pi = probability of object being present in given ith index anchor box predicted by model<\/li>\n                <li>p<sup>*<\/sup>i = being present in given ith index anchor box specified in the Label (1 positive anchor, 0 negative anchor)<\/li>\n                <li>ti = predicted bounding box for object being present (only for positive anchor and do not care for negative anchor)<\/li>\n                <li>t<sup>*<\/sup>i = ground truth of bounding box for ith positive anchor<\/li>\n                <li>L<sub>classification<\/sub> = classification loss for object present or not<\/li>\n                <li>L<sub>regression<\/sub> = regression loss for bounding<\/li>\n                <li>{pi} = output of classification layers<\/li>\n                <li>{fi} = output of regression layers<\/li>\n            <\/ul>\n            <p>\n                As referred in earlier section object detection deals with two objective viz classification and localization classification deals with whether object is present or not whose loss function is given by 1\/Nclases * Lcalsification(pi,p<sup>*<\/sup>i). Localization deals with regression loss given by 1\/Nclases and 1\/Nregression * Lregression(ti,t<sup>*<\/sup>i). Here 1\/Nregression is used to normalize and is weighted by a balancing parameter &lambda; used for balancing the trade of between the two losses. Nclasses is equal to mini batch size here we have taken 64, normalized by the number of anchor locations (i.e., Nregression~2, 400). Regression box losses are only calculated against the positive anchor boxes.\n            <\/p>\n\n            <div class=\"subsection-title\">D. Final Model outcome<\/div>\n            <p>\n                The trained models are tested over the test samples and their performance is evaluated for a few parameters such as precision, recall and IoU.\n            <\/p>\n\n            <table>\n                <caption><strong>TABLE 3: EVALUATION METRICES<\/strong><\/caption>\n                <thead>\n                    <tr>\n                        <th>Objectives<\/th>\n                        <th>Models used<\/th>\n                        <th>Avg. Precision<\/th>\n                        <th>Avg. Recall<\/th>\n                        <th>Avg. F1 Score<\/th>\n                    <\/tr>\n                <\/thead>\n                <tbody>\n                    <tr>\n                        <td>Object detection<\/td>\n                        <td>FasterRCNN- Inception V2<\/td>\n                        <td>0.453<\/td>\n                        <td>0.678<\/td>\n                        <td>0.543<\/td>\n                    <\/tr>\n                    <tr>\n                        <td><\/td>\n                        <td>SSD MobileNet V2<\/td>\n                        <td>0.352<\/td>\n                        <td>0.453<\/td>\n                        <td>0.3961<\/td>\n                    <\/tr>\n                    <tr>\n                        <td><\/td>\n                        <td>SSD_InceptionV2<\/td>\n                        <td>0.461<\/td>\n                        <td>0.384<\/td>\n                        <td>0.418<\/td>\n                    <\/tr>\n                    <tr>\n                        <td>Vehicle number plate detection<\/td>\n                        <td>FasterRCNN- Inception V2<\/td>\n                        <td>0.749302<\/td>\n                        <td>0.689<\/td>\n                        <td>0.7177<\/td>\n                    <\/tr>\n                    <tr>\n                        <td>Vehicle Model type detection<\/td>\n                        <td>VGG16<\/td>\n                        <td>0.514<\/td>\n                        <td>0.481<\/td>\n                        <td>0.496<\/td>\n                    <\/tr>\n                <\/tbody>\n            <\/table>\n        <\/div>\n\n        <div class=\"section\">\n            <div class=\"section-title\">VIII. CONCLUSIONS<\/div>\n            <p>\n                While experimenting with a few models used for vehicle detection and vehicle registration number detection and make classifier the results observed as Faster RCNN with Inception v2 and VGG16 proved to having considerably better precision, recall and F1 scores and higher accuracy against other counterparts. Table 3 gives a tabular description of all the evaluation metrices. Faster RCNN with inception v2 model is trained for 12000 epochs with momentum optimizer 0.09 and learning rate 0.00002.\n            <\/p>\n        <\/div>\n\n        <div class=\"references\">\n            <h2 class=\"section-title\" class=\"section-title\" id=\"ix-references\">IX. REFERENCES<\/h2>\n            <ol>\n                <li>MYADVO TECHSERVE PRIVATE LIMITED, \"3-minute waiting rule at tolls plazas on national highways,\" Myadvo.in. [Online]. Available: https:\/\/www.myadvo.in\/blog\/waiting-rule-at-tolls-plazas-on-national-highways\/. [Accessed: 24-Feb-2021].<\/li>\n                <li>C.-L. Huang and W.-C. Liao, \"A vision-based vehicle identification system,\" in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, 2004.<\/li>\n                <li>A. Sethi, \"Object Detection using the TensorFlow API,\" Analyticsvidhya.com, 07-Apr-2020. [Online]. Available: https:\/\/www.analyticsvidhya.com\/blog\/2020\/04\/build-your-own-object-detection-model-using-tensorflow-api\/. [Accessed: 24-Feb-2021].<\/li>\n                <li>K. Simonyan and A. Zisserman, \"Very deep convolutional networks for large-scale image recognition,\" arXiv [cs.CV], 2014.<\/li>\n                <li>R. Bose and S. Kumar, \"Hand gesture recognition using faster R-CNN inception V2 model,\" in Proceedings of the Advances in Robotics 2019, 2019.<\/li>\n                <li>\"Dr. Rushikesh P Borse,\" Mitaoe.ac.in. [Online]. Available: https:\/\/www.mitaoe.ac.in\/school-of-electrical-engineering-Dr-Rushikesh-Borse.php. [Accessed: 24-Feb-2021].<\/li>\n                <li>N. Darapaneni et al., \"Computer vision based license plate detection for automated vehicle parking management system,\" in 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2020, pp. 0800-0805.<\/li>\n                <li>D. P. Kingma and J. Ba, \"Adam: A method for stochastic optimization,\" arXiv [cs.LG], 2014.<\/li>\n                <li>V. Bushaev, \"Understanding RMSprop \u2014 faster neural network learning,\" Towards Data Science, 02-Sep-2018. [Online]. Available: https:\/\/towardsdatascience.com\/understanding-rmsprop-faster-neural-network-learning-62e116fcf29a. [Accessed: 24-Feb-2021].<\/li>\n                <li>Keras Team, \"Keras: the Python deep learning API,\" Keras.io. [Online]. Available: https:\/\/keras.io\/. [Accessed: 24-Feb-2021].<\/li>\n                <li>\"Tensorflow api Google Search,\" Google.com. [Online]. Available: https:\/\/www.google.com\/search?q=Tensorflow+api&oq=Tensorflow+api&aqs=chrome..69i57j0l4j69i60l3.2733j0j4&sourceid=chrome&ie=UTF-8. [Accessed: 24-Feb-2021].<\/li>\n                <li>\"VGG16 \u2014 convolutional network for classification and detection,\" Neurohive.io, 20-Nov-2018. [Online]. Available: https:\/\/neurohive.io\/en\/popular-networks\/vgg16. [Accessed: 24-Feb-2021].<\/li>\n                <li>S. Sharma, A. Sasi, and A. N. Cheeran, \"A SVM based character recognition system,\" in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2017, pp. 1703-1707.<\/li>\n                <li>J. C. Nascimento, A. J. Abrantes, and J. S. Marques, \"An algorithm for centroid-based tracking of moving objects,\" in 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No. 99CH36258), 1999.<\/li>\n                <li>\"OpenCV: Image Thresholding,\" Opencv.org. [Online]. Available: https:\/\/docs.opencv.org\/master\/d7\/d4d\/tutorial_py_thresholding.html. [Accessed: 24-Feb-2021].<\/li>\n                <li>N. Darapaneni, B. Krishnamurthy, and A. R. Paduri, \"Convolution Neural Networks: A Comparative Study for Image Classification,\" in 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 2020, pp. 327-332.<\/li>\n            <\/ol>\n        <\/div>\n\n    <\/div>\n<\/div>\n\n\n\n<a href=\"https:\/\/www.mygreatlearning.com\/blog\/research-and-studies\/\" \n   style=\"text-decoration: none; color: #007BFF; font-size: 16px; font-weight: bold; display: block; text-align: center; padding: 10px; margin: 0px auto 40px auto; width: fit-content;\">\n   Explore More Research and Studies\n<\/a>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Explore AI-powered traffic monitoring and toll plaza analytics to optimize traffic flow, vehicle detection, congestion analysis, and smart infrastructure.<\/p>\n","protected":false},"author":41,"featured_media":117786,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[36818],"tags":[],"class_list":["post-117770","page","type-page","status-publish","has-post-thumbnail","hentry","category-research-and-studies"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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