Abstract
This paper introduces MCTrack, a new 3D multi-object tracking method that achieves state-of-the-art (SOTA) performance across KITTI, nuScenes, and Waymo datasets. Addressing the gap in existing tracking paradigms, which often perform well on specific datasets but lack generalizability, MCTrack offers a unified solution. Additionally, we have standardized the format of perceptual results across various datasets, termed BaseVersion, facilitating researchers in the field of multi-object tracking (MOT) to concentrate on the core algorithmic development without the undue burden of data preprocessing. Finally, recognizing the limitations of current evaluation metrics, we propose a novel set that assesses motion information output, such as velocity and acceleration, crucial for downstream tasks. The source codes of the proposed method are available at this link: this https URL}{this https URL
Abstract (translated)
本文介绍了一种名为MCTrack的新3D多目标跟踪方法，在KITTI、nuScenes和Waymo数据集上实现了最先进的（SOTA）性能。解决了现有跟踪范式之间的空白，即往往在特定数据集上表现出色，但缺乏泛化性。MCTrack提供了一个统一的解决方案。此外，我们标准化了各种数据集上的感知结果格式，称为BaseVersion，便于该领域的研究人员在核心算法开发过程中集中精力，而不会过分负担数据预处理。最后，认识到当前评估指标的局限性，我们提出了一个新方法来评估运动信息输出，例如速度和加速度，这对下游任务至关重要。该方法的原型代码可在此链接中查看：https://<https://github.com/MC-MOT-Track/MC-MOT-Track>
URL
https://arxiv.org/abs/2409.16149
