Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
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
Video saliency detection with robust temporal alignment and local-global spatial contrast
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Background subtraction using low rank and group sparsity constraints
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Block-Sparse RPCA for consistent foreground detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Background subtraction, the task to detect moving objects in a scene, is an important step in video analysis. In this paper, we propose an efficient background subtraction method based on coherent trajectory decomposition. We assume that the trajectories from background lie in a low-rank subspace, and foreground trajectories are sparse outliers in this background subspace. Meanwhile, the Markov Random Field (MRF) is used to encode the spatial coherency and trajectory consistency. With the low-rank decomposition and the MRF, our method can better handle videos with moving camera and obtain coherent foreground. Experimental results on a video dataset show our method achieves very competitive performance.