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2008

Mining Appearance Models Directly From Compressed Video

9 years 3 months ago
Mining Appearance Models Directly From Compressed Video
In this paper, we propose an approach to learning appearance models of moving objects directly from compressed video. The appearance of a moving object changes dynamically in video due to varying body poses, lighting conditions, and partial occlusions. Efficiently mining the appearance models of objects is a crucial and challenging technology to support content-based video coding, clustering, indexing, and retrieval at the object level. The proposed approach learns the appearance models of moving objects in the spatial-temporal dimension of video data by taking advantage of the MPEG video compression format. It detects a moving object and recovers the trajectory of each macro-block covered by the object using the motion vector present in the compressed stream. The appearances are then reconstructed in the DCT domain along the object's trajectory, and modeled as a mixture of Gaussians (MoG) using DCT coefficients. We prove that, under certain assumptions, the MoG model learned fro...
Datong Chen, Qiang Liu, Mingui Sun, Jie Yang
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TMM
Authors Datong Chen, Qiang Liu, Mingui Sun, Jie Yang
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