Moving Objects Segmentation in Video Sequence based on Bayesian network

Paper


Duong, Thach-Thao and Duong, Anh-Duc. 2010. "Moving Objects Segmentation in Video Sequence based on Bayesian network." 8th IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future (RIVF 2010). Hanoi, Vietnam 01 - 04 Nov 2010 https://doi.org/10.1109/RIVF.2010.5633458
Paper/Presentation Title

Moving Objects Segmentation in Video Sequence based on Bayesian network

Presentation TypePaper
AuthorsDuong, Thach-Thao (Author) and Duong, Anh-Duc (Author)
Journal or Proceedings TitleProceedings of the 8th IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF 2010)
ERA Conference ID72545
Number of Pages6
Year2010
ISBN9781424480746
9781424480753
Digital Object Identifier (DOI)https://doi.org/10.1109/RIVF.2010.5633458
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/5633458
Conference/Event8th IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future (RIVF 2010)
IEEE-RIVF International Conference on Computing and Communication Technologies
Event Details
8th IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future (RIVF 2010)
Event Date
01 to end of 04 Nov 2010
Event Location
Hanoi, Vietnam
Event Details
IEEE-RIVF International Conference on Computing and Communication Technologies
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.

KeywordsBayesian network; MAP estimation; Markov random field; Moving objects; Video segmentation
ANZSRC Field of Research 2020460304. Computer vision
Byline AffiliationsVietnam National University, Vietnam
Institution of OriginUniversity of Southern Queensland
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https://research.usq.edu.au/item/q7122/moving-objects-segmentation-in-video-sequence-based-on-bayesian-network

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