Moving object extraction based on Markov random field models

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.