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

Biomedical Research
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article Open Access


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.

To read the full article Peer-reviewed Article PDF image | Peer-reviewed Full Article image

Author(s): Maha Vishnu VC Rajalakshmi M


Traffic video surveillance, Map-reduce frame work, Linear discriminate analysis (LDA) with support vector machine (SVM), Gaussian filtering method, Markov random field (MRF) with Bayesian estimation process, #

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version