Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model

  • Authors:

  • Wei Zeng;Jun Du;Wen Gao;Qingming Huang

  • Affiliations:

  • Department of Computer Science and Engineering, Harbin Institute of Technology, No. 92 West Da-Zhi Street, Harbin 150001, People's Republic of China;Graduate School of Chinese Academy of Sciences, No. 19 Yu-Quan Road, Beijing 100039, People's Republic of China;Institute of Computing Technology, Chinese Academy of Sciences, No. 8 Ke Xue Yuan Nan Road, Beijing 100080, People's Republic of China;Graduate School of Chinese Academy of Sciences, No. 19 Yu-Quan Road, Beijing 100039, People's Republic of China

  • Venue:
  • Real-Time Imaging
  • Year:
  • 2005

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Abstract

Moving object segmentation in compressed domain plays an important role in many real-time applications, e.g. video indexing, video transcoding, video surveillance, etc. Because H.264/AVC is the up-to-date video-coding standard, few literatures have been reported in the area of video analysis on H.264/AVC compressed video. Compared with the former MPEG standard, H.264/AVC employs several new coding tools and provides a different video format. As a consequence, moving object segmentation on H.264/AVC compressed video is a new task and challenging work. In this paper, a robust approach to extract moving objects on H.264/AVC compressed video is proposed. Our algorithm employs a block-based Markov Random Field (MRF) model to segment moving objects from the sparse motion vector field obtained directly from the bitstream. In the proposed method, object tracking is integrated in the uniform MRF model and exploits the object temporal consistency simultaneously. Experiments show that our approach provides the remarkable performance and can extract moving objects efficiently and robustly. The prominent applications of the proposed algorithm are object-based transcoding, fast moving object detection, video analysis on compressed video, etc.