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Road side video surveillance in traffic scenes using map-reduce framework for accident analysis

Maha Vishnu VC, Rajalakshmi M

Video surveillance and biomedical research have received a great attention in most of the active application-oriented research areas of computer vision, artificial intelligence, and image processing. Visual analysis of human motion is presently one of the active research areas in video surveillance system. Human motion analysis relates the detection, tracking and recognition of activities of the people, and more generally, the understanding of human behaviours, from image sequences involving humans. The road side traffic video surveillance aims at using several image processing methods to obtain better traffic and road safety, which in turn provides direct solution for to reducing death rate of accident victims. The distributed computing process has an efficient solution for this scalability issues in traffic video surveillance system. In this paper, a road traffic video surveillance system is proposed which can automatically identify road accidents from live video files. The system alerts neighbouring hospitals and highway rescue teams when accidents occur. This paper proposes a methodology to process the video file using map-reduce framework for better network service and better scalability solution in surveillance system. Then, the distributed video files are enhanced with Gaussian filtering. Efficient object classification and detection is carried out using Linear Discriminant Analysis (LDA) with Support Vector Machine (SVM) for traffic monitoring using video files from surveillance camera. Foreground object segmentation is a vital task which is carried out by Markov Random field (MRF) with Bayesian estimation process. This proposed method efficiently track and classify the foreground objects with use of object classification and detection and this will improve traffic monitoring in traffic scenes. The results obtained from the experiments on the proposed research shows the efficiency of traffic monitoring using traffic scenes.