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 ...
| Authors | Mei Yuan, Qi Long, Qifeng Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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