Generalized Video Anomaly Event Detection: Systematic Taxonomy and
Comparison of Deep Models
- URL: http://arxiv.org/abs/2302.05087v3
- Date: Thu, 1 Feb 2024 17:32:03 GMT
- Title: Generalized Video Anomaly Event Detection: Systematic Taxonomy and
Comparison of Deep Models
- Authors: Yang Liu, Dingkang Yang, Yan Wang, Jing Liu, Jun Liu, Azzedine
Boukerche, Peng Sun, Liang Song
- Abstract summary: Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems.
This survey extends the conventional scope of VAD beyond unsupervised methods, encompassing a broader spectrum termed Generalized Video Anomaly Event Detection (GVAED)
- Score: 33.43062232461652
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 navigates through
unsupervised, weakly-supervised, supervised and fully-unsupervised VAD
methodologies, elucidating the distinctions and interconnections within these
research trajectories. In addition, this survey facilitates prospective
researchers by assembling a compilation of research resources, including public
datasets, available codebases, programming tools, and pertinent literature.
Furthermore, this survey quantitatively assesses model performance, delves into
research challenges and directions, and outlines potential avenues for future
exploration.
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