Moving Objects Segmentation in Video Sequence based on Bayesian network

Duong, Thach-Thao ORCID: https://orcid.org/0000-0003-2294-3619 and Duong, Anh-Duc (2010) Moving Objects Segmentation in Video Sequence based on Bayesian network. In: 8th IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future (RIVF 2010), 1 Nov - 4 Nov 2010, Hanoi, Vietnam.


Abstract

This paper proposes an improvement over moving objects segmentation method for video sequence based on Bayesian network. The method integrates temporal and spatial features by Bayesian network through three fields, which are motion vector field, intensity segmentation field and object video segmentation field. Markov random field aims to push the spatial connectivity between regions. The improvement concentrates on the MAP estimation procedure in order to obtain the exact segmentation results. The Iterative MAP Estimation may cause much more error in estimation procedure and degrade the convergence of the algorithm. This paper proposes a non-iterative Estimation as an improvement for this algorithm. The non-iterative MAP estimation does not need the previous segmentation result. Therefore, the inaccurate segmentation result of former stage does not have effect on the current segmentation stage. Additionally, the non-iterative MAP estimation was designed to adapt the original model so that it does not cause failure from the theory. Experiments show that the improvement is better than the original version and has good results in some benchmark video sequences.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 30 Mar 2022 22:31
Last Modified: 30 Mar 2022 22:31
Uncontrolled Keywords: Bayesian network; MAP estimation; Markov random field; Moving objects; Video segmentation
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision
Identification Number or DOI: https://doi.org/10.1109/RIVF.2010.5633458
URI: http://eprints.usq.edu.au/id/eprint/46982

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