Real-time spatiotemporal segmentation of video objects in the H.264 compressed domain
Journal of Visual Communication and Image Representation
Compressed-domain Fall Incident Detection for Intelligent Homecare
Journal of VLSI Signal Processing Systems
Multiple moving object detection for fast video content description in compressed domain
EURASIP Journal on Advances in Signal Processing
An Approach to Trajectory Estimation of Moving Objects in the H.264 Compressed Domain
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
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Image Communication
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AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Moving object tracking in H.264/AVC bitstream
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Video-object segmentation and 3D-trajectory estimation for monocular video sequences
Image and Vision Computing
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AMR'09 Proceedings of the 7th international conference on Adaptive multimedia retrieval: understanding media and adapting to the user
Extracting moving / static objects of interest in video
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Moving object segmentation in the h.264 compressed domain
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Surveillance video synopsis in the compressed domain for fast video browsing
Journal of Visual Communication and Image Representation
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This paper addresses the problem of extracting video objects from MPEG compressed video. The only cues used for object segmentation are the motion vectors which are sparse in MPEG. A method for automatically estimating the number of objects and extracting independently moving video objects using motion vectors is presented here. First, the motion vectors are accumulated over a few frames to enhance the motion information, which are further spatially interpolated to get dense motion vectors. The final segmentation, using the dense motion vectors, is obtained by applying the expectation maximization (EM) algorithm. A block-based affine clustering method is proposed for determining the number of appropriate motion models to be used for the EM step and the segmented objects are temporally tracked to obtain the video objects. Finally, a strategy for edge refinement is proposed to extract the precise object boundaries. Illustrative examples are provided to demonstrate the efficacy of the approach. A prominent application of the proposed method is that of object-based coding, which is part of the MPEG-4 standard.