A comprehensive study of sparse codes on abnormality detection
This work addresses a gap in sparse representation methods for researchers in computer vision and anomaly detection, but it is incremental as it focuses on comparative analysis rather than introducing a new paradigm.
The authors tackled the lack of comparative studies on sparse codes for abnormality detection by comprehensively evaluating greedy algorithms and convex L1-norm solutions, finding that combining OMP codes with maximum coordinate detection achieved state-of-the-art performance on the UCSD dataset.
Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We comprehensively study two types of sparse codes solutions - greedy algorithms and convex L1-norm solutions - and their impact on abnormality detection performance. We also propose our framework of combining sparse codes with different detection methods. Our comparative experiments are carried out from various angles to better understand the applicability of sparse 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 detection could achieve state-of-the-art performance on the UCSD dataset [14].