Moving Object Tracking in Video Scenes Embedded Linux Platform
S.M Subramanian1, G. Kavya2, M. Sujatha U. Santhana Bharathy2
1S.M. Subramanian, Electronics and Communication Engineering, Prathyusha Institute of Technology and management, Chennai, India.
2G. Kavya, Electronics and Communication Engineering, Prathyusha Institute of Technology and management, Chennai, India.
3M. Sujatha, Electronics and Communication Engineering, Prathyusha Institute of Technology and management, Chennai, India.
4U.Santhana Bharathy, purusing P.G in Prathusha Institute of Technology Andmanagement, Chennai, India.
Manuscript received on November 25, 2013. | Revised Manuscript received on December 15, 2013. | Manuscript published on December 30, 2013. | PP: 53-56 | Volume-3, Issue-2, December 2013. | Retrieval Number: B2348123213/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Video tracking in real time is one of the most important topic in the field of medical. Detection and tracking of moving objects in the video scenes is the first relevant step in the information extraction in many computer vision applications. This idea can be used for the surveillance purpose, video annotation, traffic monitoring. In this paper, we are discussing about the different methods for the video trackingusing Python Opencv software and the implementation of the tracking system on the Beagleboard XM. Background Subtraction method, and color based contour tracking are the different methods using for the tracking. Andfinally, we concluded that the background subtraction method is most efficient method for tracking all the moving objects in the frames.
Keywords: Surveillance, Python opencv, Background Subtraction method, Contour tracking.