VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree

Wenlong Li, Yifei Xu, Yuan Rao, Zhenhua Wang, Shuiguang Deng

Keywords: Video Anomaly Detection, Training-Free, Generic Event Boundary, Granularity-Aware

Abstract:

Video anomaly detection (VAD) focuses on identifying anomalies in videos. Su-
pervised methods demand substantial in-domain training data and fail to deliver
clear explanations for anomalies. In contrast, training-free methods leverage
the knowledge reserves and language interactivity of large pre-trained models
to detect anomalies. However, the current fixed-length temporal window sam-
pling approaches struggle to accurately capture anomalies with varying temporal
spans. Therefore, we propose VADTree that utilizes a Hierarchical Granularity-
aware Tree (HGTree) structure for flexible sampling in VAD. VADTree leverages
the knowledge embedded in a pre-trained Generic Event Boundary Detection
(GEBD) model to characterize potential anomaly event boundaries. Specifically,
VADTree decomposes the video into generic event nodes based on boundary
confidence, and performs adaptive coarse-fine hierarchical structuring and re-
dundancy removal to construct the HGTree. Then, the multi-dimensional priors
are injected into the visual language models (VLMs) to enhance the node-wise
anomaly perception, and anomaly reasoning for generic event nodes is achieved
via large language models (LLMs). Finally, an inter-cluster node correlation
method is used to integrate the multi-granularity anomaly scores. Extensive
experiments on three challenging datasets demonstrate that VADTree achieves
state-of-the-art performance in training-free settings while drastically reducing
the number of sampled video segments. The code will be available at https:
//github.com/wenlongli10/VADTree.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 16126