Traffic Surveillance Video Summarization for Detecting Traffic Rules Violators using R-CNN

Mayya, Veena and Nayak, Aparna (2019) Traffic Surveillance Video Summarization for Detecting Traffic Rules Violators using R-CNN. In: International Conference on Computer, Communication and Computational Sciences, IC4S 2017, 11/10/2017, Kathu.

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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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Surveillance video; Convolution Neural network; Helmet detection; Deep learning; R-CNN
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 24 Jul 2019 04:58
Last Modified: 24 Jul 2019 04:58
URI: http://eprints.manipal.edu/id/eprint/154235

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