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In this paper, we propose an algorithm of anomaly detection in crowded scenes by using sparse representation over the normal bases. First, the histogram of maximal optical flow projection (HMOFP) features are extracted from a set of normal training data. Then, the online dictionary learning algorithm is used to train an optimal dictionary with proper redundancy, which is better than the dictionary simply composed by the HMOFP features of the whole training data. In order to detect the normalness of a frame, the l -norm of the sparse reconstruction coefficients is used as the Reconstruction Coefficient Sparsity (RCS). Our algorithm is effective for both global abnormal events (GAE) and local abnormal events (LAE). We evaluate our method on three benchmark datasets-the UMN dataset, the PETS2009 dataset and the UCSD Ped1 dataset. Compared with the most popular methods, experimental results show that our algorithm achieves good results especially for the pixel-level local abnormal event localization. [ABSTRACT FROM AUTHOR]

Abnormal events detection plays an important role in the video surveillance, which is a challenging subject in the intelligent detection. In this paper, based on a novel motion feature descriptor, that is, the histogram of maximal optical flow projection (HMOFP), we propose an algorithm to detect abnormal events in crowded scenes. Following the extraction of the HMOFP of the training frames, the one-class support vector machine (SVM) classification method is utilized to detect the abnormality of the testing frames. Compared with other methods based on the optical flow, experiments on several benchmark datasets show that our algorithm is effective with satisfying results. [ABSTRACT FROM AUTHOR]

• To remove the low variations and noise of objects in the background, we extract the motion descriptor of the foreground by integrating background subtraction with binarization of surveillance videos. • In the training stage, to obtain a low-rank dictionary based on the similarity of normal training samples and a compact cluster of reconstruction coefficient vectors surrounding a center in the meantime, we propose a new joint optimization of the nuclear-norm and l 2, 1 -norm. • In the detection stage, to obtain a large gap between the reconstruction errors of abnormal testing samples and those of normal testing samples, we force the reconstruction coefficient vectors of abnormal frames to distribute so that they resemble those of normal ones by solving an l 2, 1 -norm optimization problem. In this paper, a novel approach to abnormal event detection in crowded scenes is presented based on a new low-rank and compact coefficient dictionary learning (LRCCDL) algorithm. First, based on the background subtraction and binarization of surveillance videos, we construct a feature space by extracting the histogram of maximal optical flow projection (HMOFP) feature of the foreground from a normal training frame set. Second, in the training stage, a new joint optimization of the nuclear-norm and l 2, 1 -norm is applied to obtain a compact coefficient low-rank dictionary. Third, in the detection stage, l 2, 1 -norm optimization is utilized to obtain the reconstruction coefficient vectors of the testing samples. Note that the l 2, 1 -norm forces the reconstruction vectors of all the testing samples to compactly surround the same center in the training stage, such that the reconstruction errors of abnormal testing samples are different from those of normal ones. Finally, a reconstruction cost (RC) is introduced to detect abnormal frames. Experimental results on both global and local abnormal event detection show the effectiveness of our algorithm. Based on comparisons with state-of-the-art methods employing various criteria, the proposed algorithm achieves comparable detection results. [ABSTRACT FROM AUTHOR]