Structured dictionary learning for abnormal event detection in crowded scenes. (January 2018)

• Record Type: Journal Article
• Authors: Yuan, Yuan; Feng, Yachuang; Lu, Xiaoqiang
• Is Part Of: Pattern recognition. Volume 73(2018:Jan.)
• Page Start: 99
• Page End: 110
• Publication Date: 2018-01
• Subjects: Video surveillance -- Abnormal event detection -- Dictionary learning -- Sparse representation -- Reference event; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.08.001
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Dominating event patterns are discovered as reference events. Relationships among video events are described with a smoothness regularization. SR framework is constructed with reference events and smoothness regularization. The identification of abnormal events gets easier. Abstract: Abnormal event detection is now a widely concerned research topic, especially for crowded scenes. In recent years, many dictionary learning algorithms have been developed to learn normal event regularities, and have presented promising performance for abnormal event detection. However, they seldom consider the structural information, which plays important roles in many computer vision tasks, such as image denoising and segmentation. In this paper, structural information is explored within a sparse representation framework. On the one hand, we introduce a new concept named reference event, which indicates the potential event patterns in normal video events. Compared with abnormal events, normal ones are more likely to approximate these reference events. On the other hand, a smoothness regularization is constructed to describe the relationships among video events. The relationships consist of both similarities in the feature space and relative positions in the video sequences. In this case, video events related to each other are more likely to possess similar representations. The structured dictionary and sparse representation coefficients are optimized through an iterative updating strategy. In the testing phase, abnormal events are identified as samples which cannot be well represented using the learned dictionary. Extensive experiments and comparisons with state-of-the-art algorithms have been conducted to prove the effectiveness of the proposed algorithm.