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

• Published in: Real-time imaging 2005-08, Vol.11 (4), p.290-299
• Main Authors: Zeng, Wei, Du, Jun, Gao, Wen, Huang, Qingming
• Format: Article
• Language: English
• Subjects: Applied sciences; Coding, codes; Exact sciences and technology; Image processing; Information, signal and communications theory; Services and terminals of telecommunications; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telemetry. Remote supervision. Telewarning. Remote control
• ISSN: 1077-2014; 1096-116X
• DOI: 10.1016/j.rti.2005.04.008

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.