PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds

• URL: http://arxiv.org/abs/2002.11559v1
• Date: Wed, 26 Feb 2020 15:19:28 GMT
• Title: PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds
• Authors: Sukai Wang, Yuxiang Sun, Chengju Liu, Ming Liu
• Abstract summary: We propose PointTrackNet, an end-to-end 3-D object detection and tracking network. It generates foreground masks, 3-D bounding boxes, and point-wise tracking association displacements for each detected object.
• Score: 13.174385375232161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filter or particle filter) to predict object locations in a time sequence, however, they are vulnerable to extreme motion conditions, such as sudden braking and turning. In this letter, we propose PointTrackNet, an end-to-end 3-D object detection and tracking network, to generate foreground masks, 3-D bounding boxes, and point-wise tracking association displacements for each detected object. The network merely takes as input two adjacent point-cloud frames. Experimental results on the KITTI tracking dataset show competitive results over the state-of-the-arts, especially in the irregularly and rapidly changing scenarios.

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