Sparse representation has been applied successfully in abnor-mal event detection, in which the baseline is to learn a dic-tionary accompanied by sparse codes. While much empha-sis is put on discriminative dictionary construction, there areno comparative studies of sparse codes regarding abnormal-ity detection. We comprehensively study two types of sparsecodes solutions - greedy algorithms and convex L1-norm so-lutions - and their impact on abnormality detection perfor-mance. We also propose our framework of combining sparsecodes with different detection methods. Our comparative ex-periments are carried out from various angles to better un-derstand the applicability of sparse codes, including compu-tation time, reconstruction error, sparsity, detection accuracy,and their performance combining various detection methods.Experiments show that combining OMP codes with maxi-mum coordinate detection could achieve state-of-the-art per-formance on the UCSD dataset.
|Publication date||13 Mar 2016|
|Publication status||Published - 13 Mar 2016|