Adaptive Nonconvex Sparsity Based Background Subtraction for Intelligent Video Surveillance

Intelligent video surveillance is a vital technique in smart city construction, where detection of surveillance objects is generally achieved by subtracting estimated background from the raw video. Common wisdom of background estimation focuses on introducing meaningful structure or discriminative hypothesis to sparsity-based objectives. However, relaxation optimization, which is always considered a most effective solution, definitely leads to information loss. So, in this article, as to preserve more information, a new nonconvex sparsity model that can be solved directly by explicit solution is proposed for the stationary component of video. The solution, called generalized shrinkage thresholding operator, is designed by integrating the advantages of three common shrinkage operators. Then, for the regularly changing patterns, a purified dictionary learning operation is designed to find self-repeating texture patches. Eventually, foreground objects are detected by combining background subtraction with a spatiotemporal continuity constraint. Besides, built on optimizations of both models, we then show the way to refine the joint estimates using alternative optimization of all the subproblems. Experimental results have shown that, as to foreground detection task, when compared against current state-of-the-art techniques, the proposed model achieves comparable and often superior performance in terms of F-measure scores in most cases.