AN EFFCIENT SYSTEM FOR DETECTING TRAFFIC VIOLATIONS SUCH AS OVER SPEED, DISREGARDING SIGNALS, AND INSTANCES OF TRIPLE RIDING

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Mrs J. RATNA KUMARI
NADENDLA BHAVANI
SHAIK THALIB
VATLURU CHARAN NAGA SAI SURYA
BATHULA Srikanth

Abstract

In recent time surveys, the deaths and injuries due to traffic violations have increased chiefly in Indian roads. So, this needed the assistance of an automated computer vision-based object detection model, as manually identifying the vehicles violating traffic is hectic. The principle of this paper is to detect multiple violations using single video frames. The input video stream obtained from the surveillance camera is processed and annotated to carry out multiple processes. The dataset used for red-light jumping is COCO and the dataset for over boarding is created by annotating the images obtained from google. The model is trained, and the output is visualized using tensor board. The accuracy for red light skipping is 93% and the mAP value for over boarding is 0.5:0.95. This system utilizes the video stream at its maximum to detect various violations.

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How to Cite
RATNA KUMARI, M. J. ., BHAVANI, N., THALIB, S. ., SAI SURYA, V. C. N. ., & Srikanth, B. (2024). AN EFFCIENT SYSTEM FOR DETECTING TRAFFIC VIOLATIONS SUCH AS OVER SPEED, DISREGARDING SIGNALS, AND INSTANCES OF TRIPLE RIDING. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(1), 104–108. https://doi.org/10.61841/turcomat.v15i1.14548
Section
Research Articles

References

Abdulrasheed A Nasir, Jibrin O Bello, Chima K P Ofoegbu, Lukman O Abdur-Rahman, Saheed Yakub, Babatunde

A Solagberu, “Short report-Childhood motorcycle-related injuries in a Nigerian city – prevalence, spectrum and

strategies for control”, SAJCH JULY 2011

Dr. S. Raj Anand, Dr. Naveen Kilari, Dr. D. Udaya Suriya Raj Kumar,” Traffic Signal Violation Detection using

Artificial Intelligence and Deep Learning “,International Journal Advanced Research Engineering and Technology

(IJARET) of in Volume 12, Issue 2, February 2021

Krishna, Madhav Poddar, Giridhar M K and Amit Suresh Prabhu,” Automated Traffic Monitoring System Using

Computer Vision”,2016 IEEE

Chien-Yao Wang1, Alexey Bochkovskiy, and HongYuan Mark Liao1 1 Institute of Information Science, Academia

Sinica, Taiwan,” YOLOv7: Trainable bag-of freebies sets new state-of-the-art for real-time object detectors”,6 July

Ji-hun Won, Dong-hyun Lee, Kyung-min Lee, Chi-ho Lin,” An Improved YOLOv3-based Neural Network for Deidentification Technology

Abhiraj Biswas, Arka Prava Jana, Mohana, Sai Tejas S,” Classification of Objects in Video Records using Neural

Network Framework”, International Conference on Smart Systems and Inventive Technology (ICCSIT 2018) IEEE

Xplore

Suraj K Mankani, Naman S Kumar, Prasad R Dongrekar, Shreekant Sajjanar, Mohana, H V Ravish Aradhya,” RealTime Implementation of Object Detection and Tracking on DSP for Video Surveillance Applications”, IEEE

International Conference on Recent Trends in Electronics Information Communication Technology, May 20-21, 2016,

India

Mohana, HV Ravish Aradhya,” Performance Evaluation of Background Modeling Methods for Object Detection

and Tracking”, Proceedings of the Fourth International Conference on Inventive Systems and Control (ICISC 2020)

IEEE Xplore

Dr. T. Vijayakumar,” Comparative study of capsule neural network in various application”, Journal of Artificial

Intelligence and Capsule Networks (2019)

Ehsan Ayazi and Abdolreza Sheikholeslami,” Research article-A Data Mining Approach on Lorry Drivers

Overloading in Tehran Urban Roads”, Hindawi Journal of Advanced Transportation Volume 2020

Yi-Hsin Lin, Suyu Gu, Wei-Sheng Wu, Rujun Wang, and Fan Wu,” Research Article- Analysis and Prediction of

Overloaded Extra-Heavy Vehicles for Highway Safety Using Machine Learning”, Hindawi Mobile Information Systems

Volume 2020.

YOLOv7: The Fastest Object Detection Algorithm (2022) - viso.ai [13] GitHub - WongKinYiu/yolov7:

Implementation of paper - YOLOv7: Trainable bag-offreebies sets new state-of-the-art for real-time object