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Vehicle tracking is an essential task in the multi-object tracking (MOT)field. A distinct characteristic in vehicle tracking is that the trajectoriesof vehicles are fairly smooth in both the world coordinate and the imagecoordinate. Hence, models that capture motion consistencies are of highnecessity. However, tracking with the standalone motion-based trackers is quitechallenging because targets could get lost easily due to limited information,detection error and occlusion. Leveraging appearance information to assistobject re-identification could resolve this challenge to some extent. However,doing so requires extra computation while appearance information is sensitiveto occlusion as well. In this paper, we try to explore the significance ofmotion patterns for vehicle tracking without appearance information. We proposea novel approach that tackles the association issue for long-term tracking withthe exclusive fully-exploited motion information. We address the trackletembedding issue with the proposed reconstruct-to-embed strategy based on deepgraph convolutional neural networks (GCN). Comprehensive experiments on theKITTI-car tracking dataset and UA-Detrac dataset show that the proposed method,though without appearance information, could achieve competitive performancewith the state-of-the-art (SOTA) trackers. The source code will be available athttps://github.com/GaoangW/LGMTracker.

Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle Tracking