Automatic Accident Detection Techniques using CCTV Surveillance Videos: Methods, Data sets and Learning Strategies
Shilpa Jahagirdar1, Sanjay Koli2
1Shilpa Jahagirdar*, Research Scholar, Department of Electronics and Telecommunication, G. H. Raisoni College of Engineering and Management, Pune, India.
2Dr. Sanjay Koli, Research Supervisor, Department of Electronics and Telecommunication, G. H. Raisoni College of Engineering and Management, Pune, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 4282-4285 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6361029320/2020©BEIESP | DOI: 10.35940/ijeat.C6361.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Intelligent communities are utilizing different creative ideas to improve the quality of human life. Due to fast growing sizes of our cities, need of travelling is constantly increasing, which in turn has increased count of vehicles on the roads. Increasing number of vehicles on the roads has brought about numerous difficulties for Street Traffic Management Authorities. Amongst different traffic related issues, road accidents are something worth giving attention to and have to be on the priority list. This paper describes various automatic road accident detection techniques, which automatically detect accidents using surveillance videos in real-time. As these methods do not consider various lighting conditions, changing weather conditions and different traffic patterns, none of the methods are robust enough to address all the incidences of the accident. In this paper, authors have described and compared many such methods.
Keywords: Accident detection, CCTV, DNN, Surveillance videos.