Abstract
The need for long-term multi-object tracking (MOT) is growing due to the demand for analyzing individual behaviors in videos that span several minutes. Unfortunately, due to identity switches between objects, the tracking performance of existing MOT approaches decreases over time, making them difficult to apply for long-term tracking. However, in many real-world applications, such as in the livestock sector, it is possible to obtain sporadic identifications for some of the animals from sources like feeders. To address the challenges of long-term MOT, we propose a new framework that combines both uncertain identities and tracking using a Hidden Markov Model (HMM) formulation. In addition to providing real-world identities to animals, our HMM framework improves the F1 score of ByteTrack, a leading MOT approach even with re-identification, on a 10 minute pig tracking dataset with 21 identifications at the pen's feeding station. We also show that our approach is robust to the uncertainty of identifications, with performance increasing as identities are provided more frequently. The improved performance of our HMM framework was also validated on the MOT17 and MOT20 benchmark datasets using both ByteTrack and FairMOT. The code for this new HMM framework and the new 10-minute pig tracking video dataset are available at: this https URL
Abstract (translated)
由于对分析跨越几分钟视频中个体行为的需求增加，长期多目标跟踪（MOT）的重要性正在上升。不幸的是，现有的MOT方法由于对象之间的身份切换，在长时间内跟踪性能会下降，这使得它们难以用于长时跟踪。然而，在许多实际应用中，例如在畜牧业领域，可以通过诸如喂食器等来源间歇性地识别一些动物的身份。为了解决长期MOT的挑战，我们提出了一种新的框架，该框架结合了不确定身份和追踪，并利用隐马尔可夫模型（HMM）的形式化方法。 除了提供现实世界中的动物身份外，我们的HMM框架还能在10分钟猪跟踪数据集上提升ByteTrack这一领先MOT方法的F1得分，即使是在有重识别的情况下也有效。该数据集中包含21个在围栏喂食站处的身份识别信息。我们还展示了我们的方法对于身份识别不确定性的鲁棒性，并且随着提供的身份信息频率增加，性能也会相应提高。 我们在MOT17和MOT20基准测试数据集上使用ByteTrack和FairMOT验证了我们HMM框架的改进效果。此新HMM框架以及新的10分钟猪跟踪视频数据集的相关代码可以在以下网址获取：[此处插入URL]
URL
https://arxiv.org/abs/2509.09962
