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...

• Type: Journal
• Year: 2008
• Where: TMM
• Authors: Datong Chen, Qiang Liu, Mingui Sun, Jie Yang
• Keywords: Appearance Models, Mog Models, TMM 2008, Video