用雷达追踪方法改进行人跟踪，发现标准模型与数据不符。
Model-based Multi-object Visual Tracking: Identification and Standard Model Limitations
- 借鉴雷达追踪的点目标模型，用PMBM滤波计算后验分布。
- 在MOT-17数据集上验证，模型与真实数据存在明显偏差。
- 揭示模型缺陷，为未来改进提供方向，适合做跟踪算法研究者。
本文将雷达追踪领域中的多目标跟踪方法应用于基于2D边界框检测的行人跟踪问题。采用标准点目标（SPO）模型，通过泊松多伯努利混合（PMBM）滤波器计算后验密度。模型参数基于连续时间建模，包括出生概率和生存概率，部分参数从第一性原理推导，部分则通过公开的MOT-17数据集进行数据驱动识别。尽管所得PMBM算法表现良好，但揭示了SPO模型与实际数据之间的不匹配。研究认为，改进导致该不匹配的模型组件，将有助于未来构建更优的模型驱动型跟踪算法。
原文摘要 · Abstract (English)
This paper uses multi-object tracking methods known from the radar tracking community to address the problem of pedestrian tracking using 2D bounding box detections. The standard point-object (SPO) model is adopted, and the posterior density is computed using the Poisson multi-Bernoulli mixture (PMBM) filter. The selection of the model parameters rooted in continuous time is discussed, including the birth and survival probabilities. Some parameters are selected from the first principles, while others are identified from the data, which is, in this case, the publicly available MOT-17 dataset. Although the resulting PMBM algorithm yields promising results, a mismatch between the SPO model and the data is revealed. The model-based approach assumes that modifying the problematic components causing the SPO model-data mismatch will lead to better model-based algorithms in future developments.
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