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IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Automated AI Based Road Traffic Accident Alert System: YOLO Algorithm

[Full Text]



Deeksha Gour; Amit Kanskar



Vehicle detection, Deep Learning, Convolutional Neural Network,Wireless communication, Machine Learning, Python, OpenCV, YOLO



Road Accidents, a very common reason of tragic deaths and many times the victim dies due to non-reporting of such accidents to the proper authority. Since the accident was not reported the lack of emergency medical care results in death. We live in an era of technology where we are moving towards making the city, A Smart City. A smart city with smart AI based traffic monitoring and reporting mechanism can help providing medical emergencies in real time and this would result in saving lots of life. Traditional Traffic systems are equipped with IP cameras and sensors, and are already installed in most part of the city to monitor and control traffic. These systems are able to generate traffic tickets automatically. In this paper we are proposing a more advanced traffic monitoring system which can identify and detect moving objects such are cars, bikes etc in live camera feeds and detect collision of these moving objects and immediately send emergency alerts to the nearby authority for them to take necessary actions.



[1] B. Alexe, T. Deselaers, V. Ferrari, “Measuring the objectness of image windows”, TPAMI, 2012.
[2] Guzel, MS, “Versatile Vehicle Tracking and Counting Application”,KaraElmas Science and Eng Journal,7(2),622-626,2017
[3] J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, A. W. M.Smeulders, “Selective Search for Object Recognition,”International Journal of Computer Vision, Cilt. 104, s. 154–171,2013.
[4] I. Endres, D. Hoiem, "Category independent object proposals", ECCV, 2010.
[5] J. Carreira, C. Sminchisescu, “CPMC: Automatic object segmentation using constrained parametric min-cuts”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Cilt.34, s. 1312–1328, 2012.
[6] P. Arbelaez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik, "Multiscale combinatorial grouping", CVPR, 2014.
[7] D. Cires ̧an, A. Giusti, L. Gambardella, and J. Schmidhuber, "Mitosis detection in breast cancer histology images with deep neural networks", MICCAI, 2013
[8] R. Girshick, J. Donahue, T. Darrell, and J. Malik, " Rich feature hierarchies for accurate object detection and semantic segmentation.", IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2014.
[9] ImageNET Classes Date Set Avaliable at: http://imagenet.org/
[10] S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN:Towards real-time object detection with region proposal networks, NIPS, 2015.
[11] Vehicle Detection Data Set, Matlab Official Web Site Avaliable at: “https://www.mathworks. com/”, 2017.
[12] Standford Vehicle Data Set:Avaliable at: http://ai.stanford.edu/~jkrause/cars/car_dataset.Html, 2018.
[13] J. Donahue, Transferrable Represenations for Visual Recognition , PhD Thesis, University of California, Berkeley,2017,
[14] Bongjin Oh, Junhyeok Lee, A case study on scene recognition using an ensemble convolution neutral network, in 2018 20th International Conference on Advance Communication Technology (ICACT), 2018.
[15] Shristi Sonal and Saumya Suman, A Framework for Analysis Of Road Accidents, 2018 International Conference of Emerging Trends And Innovations in Engineering And Technological Research(ICETIETR).
[16] A . Krizhevsky, I. Sutskever, and G. Hinton, ImageNet Classification with Deep Convolution Neural Networks, in Advances in Neural information Processing Systems 22,pp.1106-1114,2012
[17] Lesya Anishchenko,Machine Learning in Video Surveillance for Fall Detection in Ural Symposium of Biomedical Engineering, Radio electronics and Information Technology(USBEREIT)
[18] Fall Detection from human shape and Motion History using video surveillance, in 21st International Conference on Advance Information Networking and Application Workshops (AINAW’07),2007.
[19] Lian Peng, Yimin Yang,Xiaojun Qi and Haohong Wang, Highly accurate video object identification utilizing hint information, in 2014 International Conference on Computing Networking and Communications (ICNC).
[20] P.A. Dhulekar, S.T. Gandhe, Anjali Shewale, Sayali Sonawane, Varsha Yelmame, Motion Estimation for human Activity Surveillance, in 2017 International Conference of Emerging Trends and Innovation in ICT(ICEI)
[21] Joseph Redmon , Santosh Divvala , Ross Girshich, Ali Farhadi, University of Washington, You only look once: Unified Real-time Object Detection,2016
[22] Guanqing Li, Zhiyong Song, Qiang Fu, A New Method Of Object Detection For Small Datasets Under The Framework of YOLO Network, 2018 IEEEE 3rd Advane Information Technology, Electronic and Automation Conference (IAEAC 2018)
[23] https://www.asirt.org/safe-travel/road-safety-facts/