Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/112019
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Type: Journal article
Title: Motion segmentation via a sparsity constraint
Author: Lai, T.
Wang, H.
Yan, Y.
Chin, T.
Zhao, W.
Citation: IEEE Transactions on Intelligent Transportation Systems, 2017; 18(4):973-983
Publisher: IEEE
Issue Date: 2017
ISSN: 1524-9050
1558-0016
Statement of
Responsibility: 
Taotao Lai, HanziWang, Yan Yan, Tat-Jun Chin, and Wan-Lei Zhao
Abstract: Motion segmentation is an important task for intelligent transportation systems. In this paper, inspired by the fact that a feature point trajectory can be sparsely represented as a combination of several feature point trajectories that share coherent transformations, an efficient and effective motion segmentation method with a sparsity constraint is proposed. Specifically, we first propose an accumulated scheme to efficiently integrate motion information from all the frames of a video sequence to construct a correlation matrix. Then, a sparse affinity matrix is built on the correlation matrix by using information-theoretic principles, where the nonzero elements in the same row of the sparse affinity matrix correspond to the feature point trajectories more likely belonging to the same motion. Thereafter, a segment and merge procedure is proposed to effectively estimate the number of motions via the sparse affinity matrix. Finally, by applying spectral clustering on the sparse affinitymatrix, different motions in the video sequence are accurately segmented based on the estimated number of motions. Experimental results on the Hopkins 155 and the 62-clip datasets demonstrate that the proposed method achieves superior performance compared with several state-of-the-art methods.
Keywords: Motion segmentation; affinity-based method; residual sorting; sparsity constraint
Description: Date of publication October 7, 2016; date of current version March 27, 2017.
Rights: © 2016 IEEE
RMID: 0030085526
DOI: 10.1109/TITS.2016.2596296
Grant ID: http://purl.org/au-research/grants/arc/DP160103490
Appears in Collections:Computer Science publications

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