Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark
This addresses the need for adaptive anomaly detection in video surveillance with privacy concerns, though it is incremental as it builds on existing VAD methods with a new dataset and framework.
The paper tackles the problem of human-centric video anomaly detection by introducing a new dataset (HuVAD) and an unsupervised continual learning framework (UCAL), resulting in models that achieve superior performance in 82.14% of cases and set a new state-of-the-art.
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.