Object tracking in surveillance videos using compressed-domain features from scalable bit-streams
Mehmood, K, Mrak, M, Calic, J and Kondoz, A (2009) Object tracking in surveillance videos using compressed-domain features from scalable bit-streams Signal Processing: Image Communication, 24 (10). 814 - 824. ISSN 0923-5965
Available under License : See the attached licence file.
Official URL: http://dx.doi.org/10.1016/j.image.2009.06.006
Recent developments in the video coding technology brought new possibilities of utilising inherently embedded features of the encoded bit-stream in applications such as video adaptation and analysis. Due to the proliferation of surveillance videos there is a strong demand for highly efficient and reliable algorithms for object tracking. This paper presents a new approach for the fast compressed domain analysis utilising motion data from the encoded bit-streams in order to achieve low-processing complexity of object tracking in the surveillance videos. The algorithm estimates the trajectory of video objects by using compressed domain motion vectors extracted directly from standard H.264/MPEG-4 Advanced Video Coding (AVC) and Scalable Video Coding (SVC) bit-streams. The experimental results show comparable tracking precision when evaluated against the standard algorithms in uncompressed domain, while maintaining low computational complexity and fast processing time, thus making the algorithm suitable for real time and streaming applications where good estimates of object trajectories have to be computed fast.
|Additional Information:||NOTICE: This is the author’s version of a work that was accepted for publication in Signal Processing: Image Communication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing: Image Communication, 24(10), November 2009, DOI: 10.1016/j.image.2009.06.006|
|Divisions:||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Deposited By:||Symplectic Elements|
|Deposited On:||09 May 2012 16:57|
|Last Modified:||24 Jan 2013 09:35|
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