A framework for road change detection and map updating

01-May-2017 20:32

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.The proposed work also focuses on most important field in traffic video surveillance applications namely moving object detection and classification [5].

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Visual analysis of human motion is presently one of the active research areas in video surveillance system.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 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.

Visual analysis of human motion is presently one of the active research areas in video surveillance system.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 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 results obtained from the experiments on the proposed research shows the efficiency of traffic monitoring using traffic scenes.