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Sparse representation has been applied successfully in abnormal eventdetection, in which the baseline is to learn a dictionary accompanied by sparsecodes. While much emphasis is put on discriminative dictionary construction,there are no comparative studies of sparse codes regarding abnormalitydetection. We comprehensively study two types of sparse codes solutions -greedy algorithms and convex L1-norm solutions - and their impact onabnormality detection performance. We also propose our framework of combiningsparse codes with different detection methods. Our comparative experiments arecarried out from various angles to better understand the applicability ofsparse codes, including computation time, reconstruction error, sparsity,detection accuracy, and their performance combining various detection methods.Experiments show that combining OMP codes with maximum coordinate detectioncould achieve state-of-the-art performance on the UCSD dataset [14].

A comprehensive study of sparse codes on abnormality detection