Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception.
End-to-end transformer-based algorithms, which detect and track objects
simultaneously, show great potential for the MOT task. However, most existing
methods focus on image-based tracking with a single object category. In this
paper, we propose an end-to-end transformer-based MOT algorithm (MotionTrack)
with multi-modality sensor inputs to track objects with multiple classes. Our
objective is to establish a transformer baseline for the MOT in an autonomous
driving environment. The proposed algorithm consists of a transformer-based
data association (DA) module and a transformer-based query enhancement module
to achieve MOT and Multiple Object Detection (MOD) simultaneously. The
MotionTrack and its variations achieve better results (AMOTA score at 0.55) on
the nuScenes dataset compared with other classical baseline models, such as the
AB3DMOT, the CenterTrack, and the probabilistic 3D Kalman filter. In addition,
we prove that a modified attention mechanism can be utilized for DA to
accomplish the MOT, and aggregate history features to enhance the MOD
performance.

提出了一种基于 Transformer 的多模态传感器输入的端到端多目标跟踪算法（MotionTrack），它由基于 Transformer 的数据关联（DA）模块和基于 Transformer 的查询增强模块组成，同时实现了多目标检测（MOD）。MotionTrack 及其变体在 nuScenes 数据集上获得更好的结果（AMOTA 得分为 0.55），与 AB3DMOT、CenterTrack 和概率 3D 卡尔曼滤波器等经典基线模型相比有着更好的表现。