Exploring Simple 3D Multi-Object Tracking for Autonomous Driving

• URL: http://arxiv.org/abs/2108.10312v1
• Date: Mon, 23 Aug 2021 17:59:22 GMT
• Title: Exploring Simple 3D Multi-Object Tracking for Autonomous Driving
• Authors: Chenxu Luo, Xiaodong Yang, Alan Yuille
• Abstract summary: 3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and inevitably require a matching step for the detection association. We present SimTrack to simplify the hand-crafted tracking paradigm by proposing an end-to-end trainable model for joint detection and tracking from raw point clouds.
• Score: 10.921208239968827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and inevitably require a heuristic matching step for the detection association. In this paper, we present SimTrack to simplify the hand-crafted tracking paradigm by proposing an end-to-end trainable model for joint detection and tracking from raw point clouds. Our key design is to predict the first-appear location of each object in a given snippet to get the tracking identity and then update the location based on motion estimation. In the inference, the heuristic matching step can be completely waived by a simple read-off operation. SimTrack integrates the tracked object association, newborn object detection, and dead track killing in a single unified model. We conduct extensive evaluations on two large-scale datasets: nuScenes and Waymo Open Dataset. Experimental results reveal that our simple approach compares favorably with the state-of-the-art methods while ruling out the heuristic matching rules.

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