面向自适应以人为中心的视频异常检测：一个综合框架与新基准
Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark
AI总结：
针对以人为中心的视频异常检测面临的行为复杂、异常稀缺、伦理约束等挑战，提出HuVAD隐私增强数据集与UCAL无监督持续学习框架，在多数场景下刷新SOTA。
摘要：
以人为中心的视频异常检测（VAD）旨在识别偏离正常状态的人类行为。就核心而言，以人为中心的VAD面临诸多严峻挑战，例如多样人类行为的复杂性、异常事件的稀缺性以及伦理约束。这些挑战限制了高质量数据集的获取，凸显了对支持持续学习的数据集与框架的需求。为实现自适应以人为中心的VAD，我们提出了HuVAD（以人为中心的隐私增强型视频异常检测）数据集以及一种新型无监督持续异常学习（UCAL）框架。UCAL支持增量学习，使模型能够随时间自适应，打通了传统训练与现实部署之间的壁垒。HuVAD以隐私保护为优先，提供去标识化的标注，涵盖7个室内/室外场景，其带姿态标注的帧数量是此前数据集的5倍以上。我们的标准基准与持续学习基准采用了全面的指标集，实验证明经UCAL增强的模型在82.14%的场景中取得了更优性能，刷新了当前最优水平（SOTA）。该数据集可通过https://github.com/TeCSAR-UNCC/HuVAD获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
