Delving into Motion-Aware Matching for Monocular 3D Object Tracking
Kuan-Chih Huang Ming-Hsuan Yang Yi-Hsuan Tsai
Recent advances of monocular 3D object detection facilitate the 3D
multi-object tracking task based on low-cost camera sensors. In this paper, we
find that the motion cue of objects along different time frames is critical in
3D multi-object tracking, which is less explored in existing monocular-based
approaches. In this paper, we propose a motion-aware framework for monocular 3D
MOT. To this end, we propose MoMA-M3T, a framework that mainly consists of
three motion-aware components. First, we represent the possible movement of an
object related to all object tracklets in the feature space as its motion
features. Then, we further model the historical object tracklet along the time
frame in a spatial-temporal perspective via a motion transformer. Finally, we
propose a motion-aware matching module to associate historical object tracklets
and current observations as final tracking results. We conduct extensive
experiments on the nuScenes and KITTI datasets to demonstrate that our MoMA-M3T
achieves competitive performance against state-of-the-art methods. Moreover,
the proposed tracker is flexible and can be easily plugged into existing
image-based 3D object detectors without re-training. Code and models are
available at https://github.com/kuanchihhuang/MoMA-M3T.