IEEE Trans Image Process - Video object tracking in the compressed domain using spatio-temporal Markov random fields.

Tópicos

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Resumo

Despite the recent progress in both pixel-domain and compressed-domain video object tracking, the need for a tracking framework with both reasonable accuracy and reasonable complexity still exists. This paper presents a method for tracking moving objects in H.264/AVC-compressed video sequences using a spatio-temporal Markov random field (ST-MRF) model. An ST-MRF model naturally integrates the spatial and temporal aspects of the object's motion. Built upon such a model, the proposed method works in the compressed domain and uses only the motion vectors (MVs) and block coding modes from the compressed bitstream to perform tracking. First, the MVs are preprocessed through intracoded block motion approximation and global motion compensation. At each frame, the decision of whether a particular block belongs to the object being tracked is made with the help of the ST-MRF model, which is updated from frame to frame in order to follow the changes in the object's motion. The proposed method is tested on a number of standard sequences, and the results demonstrate its advantages over some of the recent state-of-the-art methods.

Resumo Limpo

despit recent progress pixeldomain compresseddomain video object track need track framework reason accuraci reason complex still exist paper present method track move object havccompress video sequenc use spatiotempor markov random field stmrf model stmrf model natur integr spatial tempor aspect object motion built upon model propos method work compress domain use motion vector mvs block code mode compress bitstream perform track first mvs preprocess intracod block motion approxim global motion compens frame decis whether particular block belong object track made help stmrf model updat frame frame order follow chang object motion propos method test number standard sequenc result demonstr advantag recent stateoftheart method

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