O-VAD不用训练就能检测工业视频异常，跟踪对象状态变化，比传统方法更准。
O-VAD提出一种无训练、无领域知识的智能体框架，用于工业视频异常检测。该方法通过跟踪对象时空动态和状态演变，推理对象时间轨迹以识别异常。在三个IVAD数据集上，O-VAD超越了前沿视觉语言模型和传统异常检测方法。该方法提供可解释的异常过程与类型报告。
O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning
Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems. Existing VLM-based anomaly reasoning methods are capable of detecting open-ended anomalies in general domains. However, their performance declines in industrial settings characterized by intricate object transformations, strict physics, and procedural constraints. To tackle the complexity of such interaction-intensive detection, we introduce a training-free agentic framework for anomaly detection free of domain-specific knowledge, emphasizing object state evolution like humans inspectors. It is designed to track spatial-temporal dynamics and underlying transformations of detected objects over time, and then reason over the object-wise temporal state trajectories to identify abnormal objects in grounded frames. Our method overcomes limitations of prior approaches that rely on retraining on normal clips or injecting domain knowledge as context for test-time inference. Extensive experiments on three IVAD datasets demonstrate that our method outperforms frontier VLMs, agentic frameworks, and traditional VAD methods fine-tuned on the respective datasets, while providing interpretable reports over anomaly processes and types.