Video anomaly detection using deep incremental slow feature analysis network

• Published in: IET computer vision Vol. 10; no. 4; pp. 258 - 265
• Main Authors: Hu, Xing, Hu, Shiqiang, Huang, Yingping, Zhang, Huanlong, Wu, Hanbing
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.06.2016; Wiley
• Subjects: Anomalies; crowd panic; D-IncSFA network; deep incremental slow feature analysis network; deep leaning; Feature extraction; feature learning; global anomaly; hand-crafted representations; Learning; Networks; Raw; Representations; Research Article; Tasks; video anomaly detection; video data; video signal processing; hand-crafted representations; feature learning; deep incremental slow feature analysis network; D-IncSFA network; global anomaly; deep leaning; crowd panic; video signal processing; video anomaly detection; video data
• ISSN: 1751-9632; 1751-9640; 1751-9640
• DOI: 10.1049/iet-cvi.2015.0271

Existing anomaly detection (AD) approaches rely on various hand-crafted representations to represent video data and can be costly. The choice or designing of hand-crafted representation can be difficult when faced with a new dataset without prior knowledge. Motivated by feature learning, e.g. deep leaning and the ability to directly learn useful representations and model high-level abstraction from raw data, the authors investigate the possibility of using a universal approach. The objective is learning data-driven high-level representation for the task of video AD without relying on hand-crafted representation. A deep incremental slow feature analysis (D-IncSFA) network is constructed and applied to directly learning progressively abstract and global high-level representations from raw data sequence. The D-IncSFA network has the functionalities of both feature extractor and anomaly detector that make AD completion in one step. The proposed approach can precisely detect global anomaly such as crowd panic. To detect local anomaly, a set of anomaly maps, produced from the network at different scales, is used. The proposed approach is universal and convenient, working well in different types of scenarios with little human intervention and low memory and computational requirements. The advantages are validated by conducting extensive experiments on different challenge datasets.