DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network

TL;DR

DyAnNet introduces a self-trained, unsupervised video anomaly detection method that combines dynamicity and anomaly scores with a refinement process, achieving competitive results on multiple datasets.

Contribution

The paper proposes a novel unsupervised framework that fuses dynamicity and anomaly scores with a refinement strategy for improved video anomaly detection.

Findings

Achieves competitive accuracy on UCF-Crime, CCTV-Fights, and UBI-Fights datasets.

Utilizes a cross-branch I3D network for score refinement.

Effectively combines dynamicity and anomaly scores for better detection.

Abstract

Unsupervised approaches for video anomaly detection may not perform as good as supervised approaches. However, learning unknown types of anomalies using an unsupervised approach is more practical than a supervised approach as annotation is an extra burden. In this paper, we use isolation tree-based unsupervised clustering to partition the deep feature space of the video segments. The RGB- stream generates a pseudo anomaly score and the flow stream generates a pseudo dynamicity score of a video segment. These scores are then fused using a majority voting scheme to generate preliminary bags of positive and negative segments. However, these bags may not be accurate as the scores are generated only using the current segment which does not represent the global behavior of a typical anomalous event. We then use a refinement strategy based on a cross-branch feed-forward network designed using a popular I3D network to refine both scores. The bags are then refined through a segment re-mapping strategy. The intuition of adding the dynamicity score of a segment with the anomaly score is to enhance the quality of the evidence. The method has been evaluated on three popular video anomaly datasets, i.e., UCF-Crime, CCTV-Fights, and UBI-Fights. Experimental results reveal that the proposed framework achieves competitive accuracy as compared to the state-of-the-art video anomaly detection methods.