We address the problem of deformable shape and motion recovery from point correspondences in multiple perspective images. We use the low-rank shape model, i.e. the 3D shape is represented as a linear combination of unknown shape bases.We propose a new way of looking at the low-rank shape model. Instead of considering it as a whole, we assume a coarse-to-fine ordering of the deformation modes, which can be seen as a model prior. This has several advantages. First, the high level of ambiguity of the original low-rank shape model is drastically reduced since the shape bases can not anymore be arbitrarily re-combined. Second, this allows us to propose a coarse-to-fine reconstruction algorithm which starts by computing the mean shape and iteratively adds deformation modes. It directly gives the sought after metric model, thereby avoiding the difficult upgrading step required by most of the other methods. Third, this makes it possible to automatically select the number of deformation modes as the reconstruction algorithm proceeds. We propose to incorporate two other priors, accounting for temporal and spatial smoothness, which are shown to improve the quality of the recovered model parameters.The proposed model and reconstruction algorithm are successfully demonstrated on several videos and are shown to outperform the previously proposed algorithms.

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Abnormal event detection is an important issue in video surveillance applications. The goal is to detect abnormal or suspicious behaviors while given training samples that contain only normal behaviors. Sparse representation has showed its effectiveness in abnormal event detection [2,3,4,5], where a dictionary is commonly learned during training and anomalies are detected based on reconstruction error from the learned dictionary. Note that only a small proportion of the data is used for trainingrelatively to the huge amount of surveillance data, there is a high risk of incomplete normal patterns in the training data. Consequently, dictionary learning is crucial to the overall abnormality detection performance.We propose a Behavior-Specific Dictionary (BSD) algorithm, which takes into consideration the relation of atoms in one dictionary without 1,a1,2,...a1,d1,

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