提出四种融合方法提升多目标追踪的数据关联精度
FusionSORT: Fusion Methods for Online Multi-object Visual Tracking
- 融合运动、外观、高度交并比和置信度等多源线索
- 在MOT17/MOT20/DanceTrack上验证，不同融合方式效果差异显著
- 适合关注数据关联设计的视觉追踪研究者参考
本文研究了四种用于多目标视觉追踪中检测与轨迹关联的融合方法。除了考虑运动和外观等强线索外，还引入高度交并比（height-IoU）和轨迹置信度等弱线索。所考察的融合方法包括最小值、基于IoU的加权求和、卡尔曼滤波门控以及各线索代价的哈达玛乘积。我们在MOT17、MOT20和DanceTrack的验证集上进行了大量实验，发现融合方法的选择对数据关联性能至关重要。本研究旨在为计算机视觉领域提供数据关联中融合策略的实践指导。
原文摘要 · Abstract (English)
In this work, we investigate four different fusion methods for associating detections to tracklets in multi-object visual tracking. In addition to considering strong cues such as motion and appearance information, we also consider weak cues such as height intersection-over-union (height-IoU) and tracklet confidence information in the data association using different fusion methods. These fusion methods include minimum, weighted sum based on IoU, Kalman filter (KF) gating, and hadamard product of costs due to the different cues. We conduct extensive evaluations on validation sets of MOT17, MOT20 and DanceTrack datasets, and find out that the choice of a fusion method is key for data association in multi-object visual tracking. We hope that this investigative work helps the computer vision research community to use the right fusion method for data association in multi-object visual tracking.
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