Multi-object tracking is a critical component in autonomous navigation, as it provides valuable information for decision-making. Many researchers tackled the 3D multi-object tracking task by filtering out the frame-by-frame 3D detections; however, their focus was mainly on finding useful features or proper matching metrics. Our work focuses on a neglected part of the tracking system: score refinement and tracklet termination. We show that manipulating the scores depending on time consistency while terminating the tracklets depending on the tracklet score improves tracking results. We do this by increasing the matched tracklets' score with score update functions and decreasing the unmatched tracklets' score. Compared to count-based methods, our method consistently produces better AMOTA and MOTA scores when utilizing various detectors and filtering algorithms on different datasets. The improvements in AMOTA score went up to 1.83 and 2.96 in MOTA. We also used our method as a late-fusion ensembling method, and it performed better than voting-based ensemble methods by a solid margin. It achieved an AMOTA score of 67.6 on nuScenes test evaluation, which is comparable to other state-of-the-art trackers. Code is publicly available at: \url{https://github.com/cogsys-tuebingen/CBMOT}.

翻译：多球跟踪是自主导航中的一个关键组成部分,因为它为决策提供了宝贵的信息。 许多研究人员通过过滤框架三维探测来完成三维多球跟踪任务; 但是,他们的重点主要在于寻找有用的特征或适当的匹配度量。 我们的工作侧重于追踪系统中一个被忽视的部分: 评分改进和跟踪结束。 我们显示,在根据轨迹评分结束轨道时,根据时间一致性来操纵评分可以改进跟踪结果。 我们这样做的方法是增加配对的赛道的得分,配有得分更新功能,减少不匹配的赛道分。 与基于计算的方法相比,我们的方法在使用不同数据集的各种探测器和筛选算法时,始终产生更好的AMOTA和MOTA的得分。 AMOTA的得分在MOTA评分上升到1.83和2.96。 我们还使用我们的方法作为迟融合组合方法,并且用一个坚实的比基于投票的组合方法要好。 它在以67.6的AMOTA评分中, 在使用各种探测器/Cengius com 进行其他可比较的代码测试。