MCTrack统一3D多目标跟踪框架，跨数据集表现领先。
MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving
- 设计统一框架，兼顾KITTI、nuScenes、Waymo多数据集表现
- 在三个数据集上均达当前最优指标，提升追踪稳定性与泛化性
- 提供标准化感知输出格式，助力算法研发，适合自动驾驶追踪研究者
本文提出MCTrack，一种新的3D多目标跟踪方法，在KITTI、nuScenes和Waymo三个数据集上均实现当前最优性能。针对现有追踪范式在特定数据集表现好但缺乏泛化性的缺陷，MCTrack提供统一解决方案。此外，我们定义了跨数据集统一的感知结果格式（BaseVersion），使多目标追踪研究者可专注算法开发，无需承担繁重的数据预处理负担。针对现有评估指标无法有效衡量运动信息输出的问题，我们引入一套新评估标准，涵盖速度、加速度等关键运动特征，对下游任务具有重要意义。代码已开源：https://github.com/megvii-research/MCTrack
原文摘要 · Abstract (English)
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: https://github.com/megvii-research/MCTrack}{https://github.com/megvii-research/MCTrack
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