Selective Motion Estimation for Surveillance Videos
نویسندگان
چکیده
In this paper, we propose a novel approach to perform efficient motion estimation specific to surveillance videos. A real-time background subtractor is used to detect the presence of any motion activity in the sequence. Two approaches for selective motion estimation, GOP-by-GOP and Frame-byFrame, are implemented. In the former, motion estimation is performed for the whole group of pictures (GOP) only when moving object is detected for any frame of the GOP. While for the latter approach; each frame is tested for the motion activity and consequently for selective motion estimation. Experimental evaluation shows that significant reduction in computational complexity can be achieved by applying the proposed strategy.
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