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Proceedings Paper

Moving traffic object retrieval in H.264/MPEG compressed video
Author(s): Xu-li Shi; Guang Xiao; Shuo-zhong Wang; Zhao-yang Zhang; Ping An
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Paper Abstract

Moving object retrieval technique in compressed domain plays an important role in many real-time applications, e.g. Vehicle Detection and Classification. A number of retrieval techniques that operate in compressed domain have been reported in the literature. H.264/AVC is the up-to-date video-coding standard that is likely to lead to the proliferation of retrieval techniques in the compressed domain. Up to now, few literatures on H.264/AVC compressed video have been reported. Compared with the MPEG standard, H.264/AVC employs several new coding block types and different entropy coding method, which result in moving object retrieval in H.264/ AVC compressed video a new task and challenging work. In this paper, an approach to extract and retrieval moving traffic object in H.264/AVC compressed video is proposed. Our algorithm first Interpolates the sparse motion vector of p-frame that is composed of 4*4 blocks, 4*8 blocks and 8*4 blocks and so on. After forward projecting each p-frame vector to the immediate adjacent I-frame and calculating the DCT coefficients of I-frame using information of spatial intra-prediction, the method extracts moving VOPs (video object plan) using an interactive 4*4 block classification process. In Vehicle Detection application, the segmented VOP in 4*4 block-level accuracy is insufficient. Once we locate the target VOP, the actual edges of the VOP in 4*4 block accuracy can be extracted by applying Canny Edge Detection only on the moving VOP in 4*4 block accuracy. The VOP in pixel accuracy is then achieved by decompressing the DCT blocks of the VOPs. The edge-tracking algorithm is applied to find the missing edge pixels. After the segmentation process a retrieval algorithm that based on CSS (Curvature Scale Space) is used to search the interested shape of vehicle in H.264/AVC compressed video sequence. Experiments show that our algorithm can extract and retrieval moving vehicles efficiency and robustly.

Paper Details

Date Published: 12 May 2006
PDF: 8 pages
Proc. SPIE 6246, Visual Information Processing XV, 62460O (12 May 2006); doi: 10.1117/12.665346
Show Author Affiliations
Xu-li Shi, Shanghai Univ. (China)
Guang Xiao, Shanghai Municipal Education Examinations Authority (China)
Shuo-zhong Wang, Ministry of Education (China)
Zhao-yang Zhang, Shanghai Univ. (China)
Ping An, Shanghai Univ. (China)


Published in SPIE Proceedings Vol. 6246:
Visual Information Processing XV
Zia-ur Rahman; Stephen E. Reichenbach; Mark A. Neifeld, Editor(s)

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