  Vehicle tracking is an essential task in the multi-object tracking (MOT)
field. A distinct characteristic in vehicle tracking is that the trajectories
of vehicles are fairly smooth in both the world coordinate and the image
coordinate. Hence, models that capture motion consistencies are of high
necessity. However, tracking with the standalone motion-based trackers is quite
challenging because targets could get lost easily due to limited information,
detection error and occlusion. Leveraging appearance information to assist
object re-identification could resolve this challenge to some extent. However,
doing so requires extra computation while appearance information is sensitive
to occlusion as well. In this paper, we try to explore the significance of
motion patterns for vehicle tracking without appearance information. We propose
a novel approach that tackles the association issue for long-term tracking with
the exclusive fully-exploited motion information. We address the tracklet
embedding issue with the proposed reconstruct-to-embed strategy based on deep
graph convolutional neural networks (GCN). Comprehensive experiments on the
KITTI-car tracking dataset and UA-Detrac dataset show that the proposed method,
though without appearance information, could achieve competitive performance
with the state-of-the-art (SOTA) trackers. The source code will be available at
https://github.com/GaoangW/LGMTracker.
