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Open AccessArticle

Motion Segmentation Based on Model Selection in Permutation Space for RGB Sensors

The State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan 430079, China
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Sensors 2019, 19(13), 2936; https://doi.org/10.3390/s19132936
Received: 6 May 2019 / Revised: 26 June 2019 / Accepted: 30 June 2019 / Published: 3 July 2019
(This article belongs to the Section Physical Sensors)
Motion segmentation is aimed at segmenting the feature point trajectories belonging to independently moving objects. Using the affine camera model, the motion segmentation problem can be viewed as a subspace clustering problem—clustering the data points drawn from a union of low-dimensional subspaces. In this paper, we propose a solution for motion segmentation that uses a multi-model fitting technique. We propose a data grouping method and a model selection strategy for obtaining more distinguishable data point permutation preferences, which significantly improves the clustering. We perform extensive testing on the Hopkins 155 dataset, and two real-world datasets. The experimental results illustrate that the proposed method can deal with incomplete trajectories and the perspective effect, comparing favorably with the current state of the art. View Full-Text
Keywords: motion segmentation; subspace clustering; multi-model fitting; permutation preferences motion segmentation; subspace clustering; multi-model fitting; permutation preferences
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Zhao, X.; Qin, Q.; Luo, B. Motion Segmentation Based on Model Selection in Permutation Space for RGB Sensors. Sensors 2019, 19, 2936.

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