基于3D几何约束的新型框重叠度量，有效减少多目标跟踪中的ID切换。
Gr-IoU: Ground-Intersection over Union for Robust Multi-Object Tracking with 3D Geometric Constraints
- 将图像框通过消失点几何映射到地面平面，融合场景三维结构
- 在MOT17/20上相比传统方法提升关联准确率，尤其在遮挡密集场景
- 无需外观特征，适合实时多目标跟踪系统部署
我们提出一种地面交并比（Gr-IoU），以解决多目标跟踪中的数据关联问题。当摄像头检测物体时，同一物体在连续帧中常被分配不同ID，尤其是在物体靠近或重叠时。为此，我们引入Gr-IoU，利用场景的3D结构信息。该方法通过消失点几何将传统图像空间的边界框映射至地面平面，计算得到的交并比对物体前后关系更敏感，从而提升数据关联精度并减少ID切换。我们在MOT17和MOT20数据集上评估了该方法，这两个数据集包含密集人群和频繁遮挡等复杂场景。实验结果表明，Gr-IoU优于无外观特征的现有实时方法。
原文摘要 · Abstract (English)
We propose a Ground IoU (Gr-IoU) to address the data association problem in multi-object tracking. When tracking objects detected by a camera, it often occurs that the same object is assigned different IDs in consecutive frames, especially when objects are close to each other or overlapping. To address this issue, we introduce Gr-IoU, which takes into account the 3D structure of the scene. Gr-IoU transforms traditional bounding boxes from the image space to the ground plane using the vanishing point geometry. The IoU calculated with these transformed bounding boxes is more sensitive to the front-to-back relationships of objects, thereby improving data association accuracy and reducing ID switches. We evaluated our Gr-IoU method on the MOT17 and MOT20 datasets, which contain diverse tracking scenarios including crowded scenes and sequences with frequent occlusions. Experimental results demonstrated that Gr-IoU outperforms conventional real-time methods without appearance features.
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