3D Multi-Object Tracking (MOT) has achieved tremendous achievement thanks to the rapid development of 3D object detection and 2D MOT. Recent advanced works generally employ a series of object attributes, e.g., position, size, velocity, and appearance, to provide the clues for the association in 3D MOT. However, these cues may not be reliable due to some visual noise, such as occlusion and blur, leading to tracking performance bottleneck. To reveal the dilemma, we conduct extensive empirical analysis to expose the key bottleneck of each clue and how they correlate with each other. The analysis results motivate us to efficiently absorb the merits among all cues, and adaptively produce an optimal tacking manner. Specifically, we present Location and Velocity Quality Learning, which efficiently guides the network to estimate the quality of predicted object attributes. Based on these quality estimations, we propose a quality-aware object association (QOA) strategy to leverage the quality score as an important reference factor for achieving robust association. Despite its simplicity, extensive experiments indicate that the proposed strategy significantly boosts tracking performance by 2.2% AMOTA and our method outperforms all existing state-of-the-art works on nuScenes by a large margin. Moreover, QTrack achieves 48.0% and 51.1% AMOTA tracking performance on the nuScenes validation and test sets, which significantly reduces the performance gap between pure camera and LiDAR based trackers.
翻译:3D 多目标跟踪(MOT)由于3D目标探测和2D MOT的快速发展,取得了巨大的成就。最近的先进工程通常使用一系列对象属性,例如位置、大小、速度和外观,为3DMOT的关联提供线索。然而,由于某些视觉噪音,如隔离和模糊,导致跟踪性能瓶颈,这些提示可能不可靠。为了揭示进退两难,我们进行了广泛的实证分析,以揭示每个线索的关键瓶颈及其相互关系。分析结果激励我们有效地吸收所有线索的优点,并适应性地产生最佳的塔克方式。具体地说,我们介绍位置和速度质量学习,以有效指导网络估计预期目标属性的质量。根据这些质量估计,我们提议了一个质量与对象关联(QOA)战略,将质量评分作为实现强健联系的重要参考要素。尽管其简单、广泛的实验表明,拟议的战略大大提升了所有线索的优点,以2.0%运行轨道为基础,通过AMOS的测试系列和系统,以2.2%的进度跟踪了我们目前水平的进度。