Abstract
Finding reliable matches is essential in multi-object tracking to ensure the accuracy and reliability of perception systems in safety-critical applications such as autonomous vehicles. Effective matching mitigates perception errors, enhancing object identification and tracking for improved performance and safety. However, traditional metrics such as Intersection over Union (IoU) and Center Point Distances (CPDs), which are effective in 2D image planes, often fail to find critical matches in complex 3D scenes. To address this limitation, we introduce Contour Errors (CEs), an ego or object-centric metric for identifying matches of interest in tracking scenarios from a functional perspective. By comparing bounding boxes in the ego vehicle's frame, contour errors provide a more functionally relevant assessment of object matches. Extensive experiments on the nuScenes dataset demonstrate that contour errors improve the reliability of matches over the state-of-the-art 2D IoU and CPD metrics in tracking-by-detection methods. In 3D car tracking, our results show that Contour Errors reduce functional failures (FPs/FNs) by 80% at close ranges and 60% at far ranges compared to IoU in the evaluation stage.
Abstract (translated)
在多目标跟踪中，找到可靠的匹配是确保感知系统在自动驾驶等安全关键应用中的准确性和可靠性的重要因素。有效的匹配可以减少感知错误，提高物体识别和追踪的性能与安全性。然而，传统的度量标准如交并比（IoU）和中心点距离（CPD），虽然在二维图像平面上效果显著，但在复杂的三维场景中往往难以找到关键匹配。为了解决这一局限性，我们引入了轮廓误差（CEs），这是一种从功能角度出发、以自身车辆或目标为中心的度量标准，用于识别跟踪场景中的感兴趣匹配。通过比较自身车辆坐标系中的边界框，轮廓误差提供了更为功能相关的物体匹配评估。在nuScenes数据集上的广泛实验表明，与当前最优的2D IoU和CPD指标相比，轮廓误差提高了检测到追踪方法中匹配的可靠性。 特别是在三维汽车跟踪方面，我们的结果显示，在评估阶段，与IoU相比，轮廓误差可以将近距离的功能失败（FPs/FNs）减少80%，远距离的则减少60%。
URL
https://arxiv.org/abs/2506.04122
