• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.113-118
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• 2012

In this paper, we propose the removal of search point using motion vector correlation and distance between reference frames in H.264/AVC. We remove the search points in full search method and predictive motion vectors in enhanced predictive zonal search method. Since the probability that the reference frame far from the current frame is selected as the best reference frame is decreased, we apply the weighted average based on distance between the current and reference frame to determine the fianl search range. In general, the size of search range is smaller than initial search range. We reduce motion estimation time using the final search range in full search method. Also, the refinement process is adaptively applied to each reference frame. The proposed methods reduce the computational throughput of full search method by 57.13% and of enhanced predictive zonal search by 14.71% without visible performance degradation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.157-160
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• 2011

In this paper, We proposed the method of recognition and analysis of human activites using Motion vector in real-time surveillance system. We employs subtraction image techniques to detect blob(human) in the foreground. When MPEG-4 video recording EPZS(Enhanced Predicted Zonal Search) is detected the values of motion vectors were used. In this paper, the activities of human recognize and classified such as meta-classes like this {Active, Inactive}, {Moving, Non-moving}, {Walking, Running}. Each step was separated using a step-by-step threshold values. We created approximately 150 conditions for the simulation. As a result, We showed a high success rate about 86~98% to distinguish each steps in simulation image.