Anomaly Event Detection in Security Surveillance Using Two-Stream Based Model. (3rd August 2020)

• Record Type: Journal Article
• Authors: Hao, Wangli; Zhang, Ruixian; Li, Shancang; Li, Junyu; Li, Fuzhong; Zhao, Shanshan; Zhang, Wuping
• Other Names: Xu Xiaolong Academic Editor.
• Is Part Of: Security and communication networks. Volume 2020(2020)
• Publication Date: 2020-08-03
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2020/8876056
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Anomaly event detection has been extensively researched in computer vision in recent years. Most conventional anomaly event detection methods can only leverage the single-modal cues and not deal with the complementary information underlying other modalities in videos. To address this issue, in this work, we propose a novel two-stream convolutional networks model for anomaly detection in surveillance videos. Specifically, the proposed model consists of RGB and Flow two-stream networks, in which the final anomaly event detection score is the fusion of those of two networks. Furthermore, we consider two fusion situations, including the fusion of two streams with the same or different number of layers respectively. The design insight is to leverage the information underlying each stream and the complementary cues of RGB and Flow two-stream sufficiently. Two datasets (UCF-Crime and ShanghaiTech) are used to validate the effectiveness of proposed solution.