Video Anomaly Detection (VAD) can play a key role in spotting unusual activities in video footage. VAD is difficult to use in real-world settings due to the dynamic nature of human actions, environmental variations, and domain shifts. Traditional evaluation metrics often prove inadequate for such scenarios, as they rely on static assumptions and fall short of identifying a threshold that distinguishes normal from anomalous behavior in dynamic settings. To address this, we introduce an active learning framework tailored for VAD, designed for adapting to the ever-changing real-world conditions. Our approach leverages active learning to continuously select the most informative data points for labeling, thereby enhancing model adaptability. A critical innovation is the incorporation of a human-in-the-loop mechanism, which enables the identification of actual normal and anomalous instances from pseudo-labeling results generated by AI. This collected data allows the framework to define an adaptive threshold tailored to different environments, ensuring that the system remains effective as the definition of 'normal' shifts across various settings. Implemented within a lab-based framework that simulates real-world conditions, our approach allows rigorous testing and refinement of VAD algorithms with a new metric. Experimental results show that our method achieves an EBI (Error Balance Index) of 68.91 for Q3 in real-world simulated scenarios, demonstrating its practical effectiveness and significantly enhancing the applicability of VAD in dynamic environments.

翻译：视频异常检测（VAD）在识别视频片段中的异常活动方面可发挥关键作用。由于人类行为的动态性、环境变化以及领域偏移，VAD在现实场景中的应用面临困难。传统评估指标通常依赖于静态假设，难以在动态环境中确定区分正常与异常行为的阈值，因此往往不适用于此类场景。为解决这一问题，我们提出了一种专为VAD设计的主动学习框架，旨在适应不断变化的现实条件。该方法利用主动学习持续选择最具信息量的数据点进行标注，从而提升模型适应性。一个关键创新是引入了人机协同机制，该机制能够从人工智能生成的伪标签结果中识别真实的正常与异常实例。通过收集这些数据，框架可为不同环境定制自适应阈值，确保系统在"正常"定义随场景变化时仍保持有效性。我们在模拟现实条件的实验室框架中实现了该方法，支持使用新指标对VAD算法进行严格测试与优化。实验结果表明，在现实模拟场景中，我们的方法在Q3阶段实现了68.91的误差平衡指数（EBI），证明了其实际有效性，并显著提升了VAD在动态环境中的适用性。