[2405.00181] Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly

Computer Science > Computer Vision and Pattern Recognition arXiv:2405.00181 (cs) [Submitted on 30 Apr 2024 (v1), last revised 27 Mar 2026 (this version, v3)] Title:Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly Authors:Hang Du, Sicheng Zhang, Binzhu Xie, Guoshun Nan, Jiayang Zhang, Junrui Xu, Hangyu Liu, Sicong Leng, Jiangming Liu, Hehe Fan, Dajiu Huang, Jing Feng, Linli Chen, Can Zhang, Xuhuan Li, Hao Zhang, Jianhang Chen, Qimei Cui, Xiaofeng Tao View a PDF of the paper titled Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly, by Hang Du and 18 other authors View PDF HTML (experimental) Abstract:Video anomaly understanding (VAU) aims to automatically comprehend unusual occurrences in videos, thereby enabling various applications such as traffic surveillance and industrial manufacturing. While existing VAU benchmarks primarily concentrate on anomaly detection and localization, our focus is on more practicality, prompting us to raise the following crucial questions: "what anomaly occurred?", "why did it happen?", and "how severe is this abnormal event?". In pursuit of these answers, we present a comprehensive benchmark for Causation Understanding of Video Anomaly (CUVA). Specifically, each instance of the proposed benchmark involves three sets of human annotations to indicate the "what", "why" and "how" of an anomaly, including 1) anomaly type, start and end times, and e...