Moving object extraction based on Markov random field models

Zhi-ping Xie; Geng-sheng Zheng; Gui-ming He

doi:10.1117/12.655091

3 November 2005 Moving object extraction based on Markov random field models

Zhi-ping Xie, Geng-sheng Zheng, Gui-ming He

Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 604412 (2005) https://doi.org/10.1117/12.655091
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

In order to provide more efficient content-based functionalities for video applications such as content-based scalable coding, content-based indexing and retrieval, it is necessary to extract meaningful objects from scenes to enable object based representation of video content. This paper proposes an algorithm that uses Markov random field models for motion field to extract meaningful objects from video sequences, these models characterize motion of moving objects in terms of spatial interaction between motion vectors within the motion field. The proposed algorithm employs a splitting and merging procedure, in the splitting phase video frame is divided into a number of uniform regions with respect to spatial features; to detect moving objects, adjacent segmented regions are grouped together according to the motion information during the merging process, which is directed by the conditional pseudolikelihood of the motion field. The performance of the algorithm is evaluated on real world video sequences.

Citation Download Citation

Zhi-ping Xie, Geng-sheng Zheng, and Gui-ming He "Moving object extraction based on Markov random field models", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 604412 (3 November 2005); https://doi.org/10.1117/12.655091

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