Abstract
Many a times violating traffic rules leads to accidents. Many countries have adopted systems involving surveillance cameras at accident zones. Monitoring each frame to detect the violators is unrealistic. Automation of this process is highly desirable for reliable and robust monitoring of traffic rules violations. With deep learning techniques on GPU, the violation detection can be automated and performed in real time on surveillance video. This paper proposes a novel technique to summarize the traffic surveillance videos that uses Faster Regions with Convolutions Neural Networks(R-CNN) to automatically detect violators. As the proof of concept, an attempt is made to implement the proposed method to detect the two-wheeler riders without helmet. Long duration videos can be summarized into very short video that includes details about only rules violators.
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Acknowledgements
We are grateful to Mr. Ashok Rao, Chief Security Officer, MIT Manipal for providing us the access to CCTV footage that helped us to carry out the research work.
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Mayya, V., Nayak, A. (2019). Traffic Surveillance Video Summarization for Detecting Traffic Rules Violators Using R-CNN. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 759. Springer, Singapore. https://doi.org/10.1007/978-981-13-0341-8_11
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DOI: https://doi.org/10.1007/978-981-13-0341-8_11
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