Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models
University of Toronto · Fudan University · +4 more institutions
Abstract
Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos. While existing reviews predominantly concentrate on conventional unsupervised methods, they often overlook the emergence of weakly-supervised and fully-unsupervised approaches. To address this gap, this survey extends the conventional scope of VAD beyond unsupervised methods, encompassing a broader spectrum termed Generalized Video Anomaly Event Detection (GVAED). By skillfully incorporating recent advancements rooted in diverse assumptions and learning frameworks, this survey introduces an intuitive taxonomy that seamlessly…
Citation impact
- FWCI
- 35.56
- Percentile
- 100%
- References
- 247
Authors
8- YLYang LiuCorresponding
University of Toronto, Fudan University
- DYDingkang Yang
Fudan University
- YWYan Wang
Fudan University
- JLJing Liu
University of British Columbia, Singapore University of Technology and Design, Fudan University, Duke Kunshan University
- JLJun Liu
University of British Columbia, Singapore University of Technology and Design, Fudan University, Duke Kunshan University
Topics & keywords
- Computer science
- Anomaly detection
- Scope (computer science)
- Taxonomy (biology)
- Unsupervised learning
- Event (particle physics)
- Artificial intelligence
- Data science