Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection

• URL: http://arxiv.org/abs/2305.07328v1
• Date: Fri, 12 May 2023 09:03:38 GMT
• Title: Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection
• Authors: Kai Cheng, Xinhua Zeng, Yang Liu, Tian Wang, Chengxin Pang, Jing Teng, Zhaoyang Xia, and Jing Liu
• Abstract summary: Video anomaly detection (VAD) is a vital task with great practical applications in industrial surveillance, security system, and traffic control. We design a spatial-temporal hierarchical architecture (STHA) as a architecture to flexibly detect different degrees of anomaly. We conduct experiments on three benchmarks and perform extensive analysis, and the results demonstrate that our method performs comparablely to the state-of-the-art methods.
• Score: 10.956907116728267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection (VAD) is a vital task with great practical applications in industrial surveillance, security system, and traffic control. Unlike previous unsupervised VAD methods that adopt a fixed structure to learn normality without considering different detection demands, we design a spatial-temporal hierarchical architecture (STHA) as a configurable architecture to flexibly detect different degrees of anomaly. The comprehensive structure of the STHA is delineated into a tripartite hierarchy, encompassing the following tiers: the stream level, the stack level, and the block level. Specifically, we design several auto-encoder-based blocks that possess varying capacities for extracting normal patterns. Then, we stack blocks according to the complexity degrees with both intra-stack and inter-stack residual links to learn hierarchical normality gradually. Considering the multisource knowledge of videos, we also model the spatial normality of video frames and temporal normality of RGB difference by designing two parallel streams consisting of stacks. Thus, STHA can provide various representation learning abilities by expanding or contracting hierarchically to detect anomalies of different degrees. Since the anomaly set is complicated and unbounded, our STHA can adjust its detection ability to adapt to the human detection demands and the complexity degree of anomaly that happened in the history of a scene. We conduct experiments on three benchmarks and perform extensive analysis, and the results demonstrate that our method performs comparablely to the state-of-the-art methods. In addition, we design a toy dataset to prove that our model can better balance the learning ability to adapt to different detection demands.

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