Traffic Surveillance Using Image Recognition on Distributed Platform
Abstract: With the spread of low-priced, lightweight video cameras the amount of video data generated has increased tremendously. These days surveillance cameras are installed everywhere from enterprises to public places in traffic signals etc. Generally these clips are stored as video files corresponding to several hours. Now, there is a need to process such huge amount of data mostly to recognize and detect certain objects within a scene. The major challenges faced with video data are that it's unstructured with high volume. Distributed computing is one of the techniques that can be used to efficiently analyze such large amount of data. In this paper we have proposed a Hadoop-Mapreduce based model to carry out video surveillance. In this paper, we have proposed an architecture that creates image frames from videos and then applies image recognition algorithm using MapReduce. As an use case we have discussed the application of the proposed surveillance system for processing video data captured at real-time traffic scene, CCTV footages where there is a demand for sophisticated software systems.
Keywords: Distributed Computing, Hadoop, Map-Reduce, HiPi, Video Surveillance, Traffic Surveillance, ffmpeg
How to Cite?: Tapan Kumar Hazra, Shaif Choudhury, Soummyo Priyo Chattopadhyay, "Traffic Surveillance Using Image Recognition on Distributed Platform", Volume 6 Issue 4, April 2017, International Journal of Science and Research (IJSR), Pages: 612-616, https://www.ijsr.net/getabstract.php?paperid=ART20172256, DOI: https://dx.doi.org/10.21275/ART20172256