IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Alternating Projections on Manifolds
Mathematics of Operations Research
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Learning incoherent sparse and low-rank patterns from multiple tasks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Direct Robust Matrix Factorizatoin for Anomaly Detection
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust principal component analysis with non-greedy l1-norm maximization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Robust Matrix Decomposition With Sparse Corruptions
IEEE Transactions on Information Theory
Computer Vision: Models, Learning, and Inference
Computer Vision: Models, Learning, and Inference
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Dual-force metric learning for robust distracter-resistant tracker
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Video segmentation with superpixels
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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In low-rank & sparse matrix decomposition, the entries of the sparse part are often assumed to be i.i.d. sampled from a random distribution. But the structure of sparse part, as the central interest of many problems, has been rarely studied. One motivating problem is tracking multiple sparse object flows (motions) in video. We introduce "shifted subspaces tracking (SST)" to segment the motions and recover their trajectories by exploring the low-rank property of background and the shifted subspace property of each motion. SST is composed of two steps, background modeling and flow tracking. In step 1, we propose "semi-soft GoDec" to separate all the motions from the low-rank background L as a sparse outlier S. Its soft-thresholding in updating S significantly speeds up GoDec and facilitates the parameter tuning. In step 2, we update X as S obtained in step 1 and develop "SST algorithm" further decomposing X as X = Σi=1k L(i)oτ(i)+ S+G, wherein L(i) is a low-rank matrix storing the ith flow after transformation τ(i). SST algorithm solves k sub-problems in sequel by alternating minimization, each of which recovers one L(i) and its τ(i) by randomized method. Sparsity of L(i) and between-frame affinity are leveraged to save computations. We justify the effectiveness of SST on surveillance video sequences.