Abnormal Event Detection via Compact Low-Rank Sparse Learning

Abstract

Sparsity-based methods have been recently applied to abnormal event detection, and have achieved impressive results. However, most such methods fail to consider the relationship among coefficient vectors; furthermore, they neglect the underlying "dictionary structure."' The authors' compact low-rank sparse representation (CLSR) method overcomes these drawbacks. Specifically, it adds compact regularization to the sparse representation model, which explicitly considers the relationship among coefficient vectors. The authors utilize the low-rank property to capture the underlying dictionary structure. Their method is verified on three challenging databases, and the experimental results demonstrate that it compares favorably to state-of-the-art methods in abnormal event detection.