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-
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- PossiblePossibly related (embedding) · 51%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
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“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
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- LinkedLinked via arxiv author · 85%Mei Yuan →
“O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning”
- LinkedLinked via arxiv author · 85%Qi Long →
“O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning”
- LinkedLinked via arxiv author · 85%Qifeng Wu →
“O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning”