A New Contour-Based Approach to Moving Object Detection and Tracking Using a Low-End Three-Dimensional Laser Scanner

计算机视觉 扫描仪 激光扫描 人工智能 跟踪(教育) 目标检测 计算机科学 激光器 光学 模式识别(心理学) 物理 心理学 教育学
作者
Jhonghyun An,Baehoon Choi,Hyun-Ju Kim,Euntai Kim
出处
期刊:IEEE Transactions on Vehicular Technology [Institute of Electrical and Electronics Engineers]
卷期号:68 (8): 7392-7405 被引量:10
标识
DOI:10.1109/tvt.2019.2924268
摘要

Unlike high-end three-dimensional (3-D) scanners with more than 16 layers which are mainly used in academia, low-end 3-D scanners with a few layers are being developed by sensor makers for installation in commercial advanced driver assistance system. The output of a low-end 3-D scanner is completely different from that of a full 3-D scanner and it is rather similar to the output of a 2-D scanner with a single layer. In this paper, a new framework for moving object detection and subsequent tracking using a low-end 3-D scanner with four layers is proposed. The proposed method uses the contours of the objects to obtain a robust association between a detection and a tracking. The proposed method comprises five steps: preprocessing, contour extraction, hypothesis generation, pruning, and moving object detection. In the preprocessing step, outliers, such as the ground or backlights from preceding vehicles, are removed and the scanned points are decomposed into segments, each of which corresponds to a single object. In the track hypothesis generation step, each segment is associated with an existing track maintained over multiple scans. The association method developed here uses the contour shape of the segments and is motivated by the linear programming and dynamic time warping. In the track hypothesis pruning step, unlikely tracks are removed from the hypothesis trees based on the proposed hypothesis scores. In the last step, moving objects are detected based on the track velocity. The proposed method is applied to four challenging real-world scenarios, and its validity is demonstrated via experimentation.
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