用隐马尔可夫模型解决牲畜长期追踪中的身份混淆问题
An HMM-based framework for identity-aware long-term multi-object tracking from sparse and uncertain identification: use case on long-term tracking in livestock
- 基于隐马尔可夫模型融合稀疏身份信息与跟踪结果
- 在10分钟猪只追踪数据集上使ByteTrack的F1分数提升
- 适用于身份信息不连续但有规律出现的真实场景
随着对视频中个体行为分析需求的增长，长时多目标追踪（MOT）的重要性日益凸显。然而，现有方法因目标间频繁发生身份切换，导致追踪性能随时间下降，难以用于长期追踪。在畜牧等领域，可通过饲喂站等设备获取部分动物的零星身份信息。为此，本文提出一种基于隐马尔可夫模型（HMM）的新框架，融合不确定身份信息与追踪结果。在包含21次身份识别的10分钟猪只追踪数据集上，该框架显著提升了ByteTrack的F1分数。同时，实验表明其对身份信息不确定性具有鲁棒性，识别频率越高，性能越优。该方法在MOT17和MOT20基准数据集上也验证了有效性，支持ByteTrack与FairMOT。代码与新数据集已公开。
原文摘要 · Abstract (English)
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: https://github.com/ngobibibnbe/uncertain-identity-aware-tracking
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