Online learning for human classification in 3D LiDAR-based tracking

Yan, Zhi and Duckett, Tom and Bellotto, Nicola (2017) Online learning for human classification in 3D LiDAR-based tracking. In: IEEE/RSJ International Conference on Itelligent Robots and Systems (IROS), 24-28 Sep 2017, Vancouver, Canada.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Human detection and tracking is one of the most important aspects to be considered in service robotics, as the robot often shares its workspace and interacts closely with humans. This paper presents an online learning framework for human classification in 3D LiDAR scans, taking advantage of robust multi-target tracking to avoid the need for data annotation by a human expert. The system learns iteratively by retraining a classifier online with the samples collected by the robot over time. A novel aspect of our approach is that errors in training data can be corrected using the information provided by the 3D LiDAR-based tracking. In order to do this, an efficient 3D cluster detector of potential human targets has been implemented. We evaluate the framework using a new 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The experiments analyse the real-time performance of the cluster detector and show that our online-trained human classifier matches and in some cases outperforms its offline version.

Keywords:mobile robotics, online learning, human tracking, human detection
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
ID Code:27675
Deposited On:16 Jun 2017 10:56

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