ArXiv

O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning

2026-07-20 · 16:36 UTC ·Mei Yuan, Qi Long, Qifeng Wu... ·1 min read
Authors
Mei Yuan, Qi Long, Qifeng Wu...
Categories
cs.CV, cs.AI, cs.CL, cs.MA
arXiv
https://arxiv.org/abs/2607.18142v1
PDF
https://arxiv.org/pdf/2607.18142v1

Brief

O-VAD presents a training-free, agentic approach for Industrial Video Anomaly Detection that focuses on object-centric tracking and reasoning: it models objects' spatial–temporal state evolution and analyzes object-wise temporal trajectories to localize abnormal objects. Evaluated on three IVAD datasets, it outperforms frontier VLMs, other agentic methods, and fine-tuned traditional VADs while producing interpretable anomaly reports (abstract only).

Why it matters

O-VAD is a training-free, agentic, object-centric framework that tracks spatial–temporal object state evolution and reasons over object-wise temporal state trajectories to detect and ground anomalous objects in industrial video frames.

Key details

  • On three industrial VAD (IVAD) datasets, O-VAD reportedly outperforms frontier vision–language models, prior agentic frameworks, and traditional VAD methods fine-tuned on the respective datasets (abstract provides no numeric metrics).
  • The method delivers interpretable reports on anomaly processes and types without domain-specific retraining or context injection; paper by Mei Yuan et al. was accepted to ECCV 2026.
Source evidence

Abstract

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.

Comment: Accepted to ECCV 2026