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Abstract
En 中文
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 (1)-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.
Keywords:
HMOFP
Online dictionary learning
Sparse representation
Abnormal events
Crowded scenes
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3
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