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[2602.12983] Detecting Object Tracking Failure via Sequential Hypothesis Testing
Summary
This paper presents a method for detecting object tracking failures using sequential hypothesis testing, enhancing safety in computer vision applications.
Why It Matters
Object tracking is crucial in various fields, including surveillance and robotics. This research addresses the lack of formal safety measures in existing systems, proposing a statistically grounded approach to reliably identify tracking failures, which can prevent costly errors and improve system robustness.
Key Takeaways
- Introduces a sequential hypothesis testing framework for object tracking failure detection.
- The method is computationally efficient and model-agnostic, requiring no additional training.
- Demonstrates effectiveness across multiple video benchmarks and tracking models.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.12983 (cs) [Submitted on 13 Feb 2026] Title:Detecting Object Tracking Failure via Sequential Hypothesis Testing Authors:Alejandro Monroy Muñoz, Rajeev Verma, Alexander Timans View a PDF of the paper titled Detecting Object Tracking Failure via Sequential Hypothesis Testing, by Alejandro Monroy Mu\~noz and 2 other authors View PDF HTML (experimental) Abstract:Real-time online object tracking in videos constitutes a core task in computer vision, with wide-ranging applications including video surveillance, motion capture, and robotics. Deployed tracking systems usually lack formal safety assurances to convey when tracking is reliable and when it may fail, at best relying on heuristic measures of model confidence to raise alerts. To obtain such assurances we propose interpreting object tracking as a sequential hypothesis test, wherein evidence for or against tracking failures is gradually accumulated over time. Leveraging recent advancements in the field, our sequential test (formalized as an e-process) quickly identifies when tracking failures set in whilst provably containing false alerts at a desired rate, and thus limiting potentially costly re-calibration or intervention steps. The approach is computationally light-weight, requires no extra training or fine-tuning, and is in principle model-agnostic. We propose both supervised and unsupervised variants by leveraging either ground-truth or solely interna...
