Contour Errors: An Ego-Centric Metric for Reliable 3D Multi-Object Tracking
This addresses the need for more reliable perception in safety-critical applications like autonomous vehicles, though it is an incremental improvement over existing metrics.
The paper tackled the problem of unreliable matches in 3D multi-object tracking for autonomous vehicles by introducing Contour Errors, which reduced functional failures by 80% at close ranges and 60% at far ranges compared to IoU on the nuScenes dataset.
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.