Adaptive-Motion-Detector-Based Skip-Mode Predecision in Motion Estimation for Video Surveillance

DOI Web Site 参考文献18件
  • Su Jia
    Graduate school of Information, Production, System, Waseda University
  • Ikenaga Takeshi
    Graduate school of Information, Production, System, Waseda University

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抄録

H.264/AVC often leads to high computational complexity, which induces high power dissipation when a tremendous number of cameras is installed to build a large-scale surveillance system. Moreover, there is much background video compression redundancy in the surveillance scene. To solve this problem from both computer vision and video compression points of view, we propose a multi-objective optimized motion block detector for skip-mode predetermination in motion estimation H.264/AVC. The proposed algorithm contains two stages: multi-objective-optimization-based motion block detector and an application in the skip-mode decision of the fast motion estimation algorithm for video surveillance scenes. In the prestage, a motion detector with an enhanced difference filter and weighted erosion filter was designed, in which multi-objective optimization was introduced for automatically determining the weights for each filter. For the uninterested static macroblock, a low-bit-rate skip mode can be directly chosen as the best mode while performing motion estimation. Compared with the statistical and knowledge-based object detector (SAKBOT), the proposed motion detector can achieve a time saving of more than 40% on average, while attaining a high accuracy for most indoor and outdoor surveillance test sequences. Compared with the UMHexagonS fast block matching method of JM11.0, it can achieve a time saving of 23%-41% in motion estimation (ME) and higher detection accuracy for uncompressed surveillance videos.

収録刊行物

  • 信号処理

    信号処理 16 (1), 67-78, 2012

    信号処理学会

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