Multi-object tracking is a cornerstone capability of any robotic system. The quality of tracking is largely dependent on the quality of the detector used. In many applications, such as autonomous vehicles, it is preferable to over-detect objects to avoid catastrophic outcomes due to missed detections. As a result, current state-of-the-art 3D detectors produce high rates of false-positives to ensure a low number of false-negatives. This can negatively affect tracking by making data association and track lifecycle management more challenging. Additionally, occasional false-negative detections due to difficult scenarios like occlusions can harm tracking performance. To address these issues in a unified framework, we propose to learn shape and spatio-temporal affinities between tracks and detections in consecutive frames. Our affinity provides a probabilistic matching that leads to robust data association, track lifecycle management, false-positive elimination, false-negative propagation, and sequential track confidence refinement. Though past 3D MOT approaches address a subset of components in this problem domain, we offer the first self-contained framework that addresses all these aspects of the 3D MOT problem. We quantitatively evaluate our method on the nuScenes tracking benchmark where we achieve 1st place amongst LiDAR-only trackers using CenterPoint detections. Our method estimates accurate and precise tracks, while decreasing the overall number of false-positive and false-negative tracks and increasing the number of true-positive tracks. We analyze our performance with 5 metrics, giving a comprehensive overview of our approach to indicate how our tracking framework may impact the ultimate goal of an autonomous mobile agent. We also present ablative experiments and qualitative results that demonstrate our framework's capabilities in complex scenarios.

翻译：多目标跟踪是任何机器人系统的基石能力。跟踪质量在很大程度上依赖于检测器的性能。在自动驾驶等应用场景中，为避免漏检导致灾难性后果，通常倾向于过度检测目标。因此，当前最先进的3D检测器通过产生高误检率来确保低漏检率。这会增加数据关联和轨迹生命周期管理的难度，进而对跟踪产生负面影响。此外，由遮挡等困难场景造成的偶发性漏检也会损害跟踪性能。为在统一框架中解决这些问题，我们提出学习连续帧间轨迹与检测结果的形状及时空亲和度。该亲和度提供概率匹配机制，可实现鲁棒的数据关联、轨迹生命周期管理、误检消除、漏检传播及轨迹置信度序贯优化。尽管过往3D多目标跟踪方法仅涉及该问题域的部分组件，我们首次提出涵盖3D多目标跟踪所有环节的独立框架。在nuScenes跟踪基准上进行的定量评估表明，采用CenterPoint检测结果时，本方法在纯激光雷达跟踪器中位列第一。我们的方法在降低总误检/漏检轨迹数、提升正确轨迹数的同时，可估计出高精度高准确度轨迹。我们使用5项指标进行性能分析，全面展示方法特性，阐明跟踪框架对自主移动智能体最终目标的影响。通过消融实验和定性结果，进一步验证了框架在复杂场景下的能力。