用统计检验方法实时检测跟踪失败，确保系统可靠不误报。
Detecting Object Tracking Failure via Sequential Hypothesis Testing
- 将跟踪视为序列假设检验，逐步积累失败证据。
- 在四个数据集上验证，误报率可控且响应迅速。
- 无需额外训练，通用性强，适合部署于各类跟踪模型。
实时视频目标跟踪是计算机视觉的核心任务，广泛应用于视频监控、动作捕捉和机器人等领域。现有跟踪系统通常缺乏正式的安全保障，仅依赖启发式置信度指标发出警告。本文提出将目标跟踪建模为序列假设检验，通过逐步累积失败证据来判断跟踪是否失效。基于最新进展，所提方法（形式化为e-process）能快速识别跟踪失败，同时在理论上保证误报率不超过设定阈值，从而减少不必要的重新校准或干预。该方法计算轻量，无需额外训练或微调，原则上对任意跟踪模型均适用。我们设计了利用真实标注信息的监督型与仅依赖内部跟踪信息的无监督型两种变体，并在两个主流跟踪模型及四个视频基准上验证了其有效性。结果表明，序列检验可为实时跟踪系统提供兼具统计严谨性与高效性的安全保障。
原文摘要 · Abstract (English)
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 internal tracking information, and demonstrate its effectiveness for two established tracking models across four video benchmarks. As such, sequential testing can offer a statistically grounded and efficient mechanism to incorporate safety assurances into real-time tracking systems.
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