A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Curvature Clustering (SCC)
International Journal of Computer Vision
On rank correlation and the distance between rankings
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Generalized distances between rankings
Proceedings of the 19th international conference on World wide web
Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Video segmentation by tracing discontinuities in a trajectory embedding
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Higher order motion models and spectral clustering
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Latent Low-Rank Representation for subspace segmentation and feature extraction
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Spatio-temporal clustering of probabilistic region trajectories
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Motion segmentation refers to the problem of separating the objects in a video sequence according to their motion. It is a fundamental problem of computer vision, since various systems focusing on the analysis of dynamic scenes include motion segmentation algorithms. In this paper we present a novel approach, where a video shot is temporally divided in successive and overlapping windows and motion segmentation is performed on each window respectively. This attribute renders the algorithm suitable even for long video sequences. In the last stage of the algorithm the segmentation results for every window are aggregated into a final segmentation. The presented algorithm can handle effectively asynchronous trajectories on each window even when they have no temporal intersection. The evaluation of the proposed algorithm on the Berkeley motion segmentation benchmark demonstrates its scalability and accuracy compared to the state of the art.