Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark

• URL: http://arxiv.org/abs/2408.14329v2
• Date: Wed, 19 Mar 2025 18:13:10 GMT
• Title: Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark
• Authors: Armin Danesh Pazho, Shanle Yao, Ghazal Alinezhad Noghre, Babak Rahimi Ardabili, Vinit Katariya, Hamed Tabkhi,
• Abstract summary: Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal.<n>We introduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection) dataset and a novel Unsupervised Continual Anomaly Learning framework.
• Score: 2.473948454680334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal. At its core, human-centric VAD faces substantial challenges, such as the complexity of diverse human behaviors, the rarity of anomalies, and ethical constraints. These challenges limit access to high-quality datasets and highlight the need for a dataset and framework supporting continual learning. Moving towards adaptive human-centric VAD, we introduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection) dataset and a novel Unsupervised Continual Anomaly Learning (UCAL) framework. UCAL enables incremental learning, allowing models to adapt over time, bridging traditional training and real-world deployment. HuVAD prioritizes privacy by providing de-identified annotations and includes seven indoor/outdoor scenes, offering over 5x more pose-annotated frames than previous datasets. Our standard and continual benchmarks, utilize a comprehensive set of metrics, demonstrating that UCAL-enhanced models achieve superior performance in 82.14% of cases, setting a new state-of-the-art (SOTA). The dataset can be accessed at https://github.com/TeCSAR-UNCC/HuVAD.

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