Multibody Structure-and-Motion Segmentation by Branch-and-Bound Model Selection

2010 ◽  
Vol 19 (6) ◽  
pp. 1393-1402 ◽  
Author(s):  
Ninad Thakoor ◽  
Jean Gao ◽  
Venkat Devarajan
Keyword(s):  
Model Selection ◽  
Branch And Bound ◽  
Motion Segmentation ◽  
Structure And Motion

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

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2936 ◽  
Author(s):  
Xi Zhao ◽  
Qianqing Qin ◽  
Bin Luo
Keyword(s):  
Model Selection ◽  
Moving Objects ◽  
Motion Segmentation ◽  
Model Fitting ◽  
Selection Strategy ◽  
Camera Model ◽  
Clustering Problem ◽  
Point Trajectories ◽  
Real World Datasets ◽  
Low Dimensional

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.

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