Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2024

Abstract

Current video anomaly detection (VAD) approaches with weak supervisions are inherently limited to a closed-set setting and may struggle in open-world applications where there can be anomaly categories in the test data unseen during training. A few recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to de-tect unseen anomalies given seen anomalies and normal videos. However, such a setting focuses on predicting frame anomaly scores, having no ability to recognize the specific categories of anomalies, despite the fact that this ability is essential for building more informed video surveillance systems. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pretrained large models to detect and cate-gorize seen and unseen anomalies. To this end, we propose a model that decouples OVVAD into two mutually comple-mentary tasks - class-agnostic detection and class-specific classification - and jointly optimizes both tasks. Particu-larly, we devise a semantic knowledge injection module to introduce semantic knowledge from large language models for the detection task, and design a novel anomaly synthesis module to generate pseudo unseen anomaly videos with the help of large vision generation models for the classification task. These semantic knowledge and synthesis anomalies substantially extend our model's capability in detecting and categorizing a variety of seen and unseen anomalies. Exten-sive experiments on three widely-used benchmarks demonstrate our model achieves state-of-the-art performance on OVVAD task.

Keywords

Video anomaly detection, Anomalies categorization, Open-vocabulary video anomaly detection, Semantic knowledge injection

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024) : Seattle, WA, USA, June 16-22

Identifier

10.1109/CVPR52733.2024.01732

Publisher

IEEE

City or Country

Seattle

Additional URL

https://doi.org/10.1109/CVPR52733.2024.01732

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