A new contour-based approach to moving object detection and tracking using a low-end three-dimensional laser scanner

Jhonghyun An, Baehoon Choi, Hyunju Kim, Euntai Kim

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11 Citations (Scopus)


Unlike high-end three-dimensional (3-D) scanners with more than 16 layers which are mainly used in academia, lowend 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 formoving 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 trackmaintained 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.

Original languageEnglish
Article number8743409
Pages (from-to)7392-7405
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Issue number8
Publication statusPublished - 2019 Aug

Bibliographical note

Funding Information:
Manuscript received November 10, 2018; revised March 25, 2019, May 7, 2019, and June 11, 2019; accepted June 11, 2019. Date of publication June 21, 2019; date of current version August 13, 2019. This work was supported by Industry-Academy Collaboration Project (Development of laser-scanner based recognition technology for intersection safety) funded by the Hyundai Motor Company, Korea. The review of this paper was coordinated by Dr. Z. Liu. (Corresponding author: Euntai Kim.) J. An, B. Choi, and E. Kim are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea (e-mail: jhonghyen@yonsei.ac.kr; choibae@yonsei.ac.kr; hyunju.kim@hyundai.com).

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


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