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Optical Engineering

Moving object segmentation algorithm based on cellular neural networks in the H.264 compressed domain
Author(s): Feng Jie; Yaowu Chen; Xiang Tian
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Paper Abstract

A cellular neural network (CNN)-based moving object segmentation algorithm in the H.264 compressed domain is proposed. This algorithm mainly utilizes motion vectors directly extracted from H.264 bitstreams. To improve the robustness of the motion vector information, the intramodes in I-frames are used for smooth and nonsmooth region classification, and the residual coefficient energy of P-frames is used to update the classification results first. Then, an adaptive motion vector filter is used according to interpartition modes. Finally, many CNN models are applied to implement moving object segmentation based on motion vector fields. Experiment results are presented to verify the efficiency and the robustness of this algorithm.

Paper Details

Date Published: 1 July 2009
PDF: 7 pages
Opt. Eng. 48(7) 077001 doi: 10.1117/1.3158987
Published in: Optical Engineering Volume 48, Issue 7
Show Author Affiliations
Feng Jie, Zhejiang Univ. (China)
Yaowu Chen, Zhejiang Univ. (China)
Xiang Tian, Zhejiang Univ. (China)

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