Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles

Hao Chen; Chunyue Xue; Shoubin Liu; Yuxiang Sun; Yongquan Chen

doi:10.1109/ROBIO49542.2019.8961438

Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles

Hao Chen, Chunyue Xue, Shoubin Liu, Yuxiang Sun, Yongquan Chen^*

^*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

7 Citations (Scopus)

Abstract

This article describes a multi-object tracking method through sensor fusion with a monocular camera and a 3-D Lidar for autonomous vehicles. Specifically, several pairwise costs from information, such as locations, movements, and poses of 3-D cues, are designed for tracking. These costs can complement each other to reduce matching errors during the tracking process. Moreover, they are efficient to be on-line computed with embedded equipment. We feed the pairwise costs to the data-association framework, which is based on the Hungarian algorithm, and then do the back-end fusion for the tracking results. The experimental results on our autonomous sightseeing car demonstrate that our tracking method could achieve accurate and robust results in real-world traffic scenarios.

Original language	English
Title of host publication	IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	456-460
Number of pages	5
ISBN (Electronic)	9781728163215
DOIs	https://doi.org/10.1109/ROBIO49542.2019.8961438
Publication status	Published - Dec 2019
Externally published	Yes
Event	2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 - Dali, China Duration: 6 Dec 2019 → 8 Dec 2019

Publication series

Name	IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

Conference

Conference	2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
Country/Territory	China
City	Dali
Period	6/12/19 → 8/12/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Access to Document

10.1109/ROBIO49542.2019.8961438

Cite this

Chen, H., Xue, C., Liu, S., Sun, Y., & Chen, Y. (2019). Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles. In IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 (pp. 456-460). Article 8961438 (IEEE International Conference on Robotics and Biomimetics, ROBIO 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROBIO49542.2019.8961438

@inproceedings{196a3a01cd614a1fb8cbc168fa32e69f,

title = "Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles",

abstract = "This article describes a multi-object tracking method through sensor fusion with a monocular camera and a 3-D Lidar for autonomous vehicles. Specifically, several pairwise costs from information, such as locations, movements, and poses of 3-D cues, are designed for tracking. These costs can complement each other to reduce matching errors during the tracking process. Moreover, they are efficient to be on-line computed with embedded equipment. We feed the pairwise costs to the data-association framework, which is based on the Hungarian algorithm, and then do the back-end fusion for the tracking results. The experimental results on our autonomous sightseeing car demonstrate that our tracking method could achieve accurate and robust results in real-world traffic scenarios.",

author = "Hao Chen and Chunyue Xue and Shoubin Liu and Yuxiang Sun and Yongquan Chen",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 ; Conference date: 06-12-2019 Through 08-12-2019",

year = "2019",

month = dec,

doi = "10.1109/ROBIO49542.2019.8961438",

language = "English",

series = "IEEE International Conference on Robotics and Biomimetics, ROBIO 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "456--460",

booktitle = "IEEE International Conference on Robotics and Biomimetics, ROBIO 2019",

}

Chen, H, Xue, C, Liu, S, Sun, Y & Chen, Y 2019, Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles. in IEEE International Conference on Robotics and Biomimetics, ROBIO 2019., 8961438, IEEE International Conference on Robotics and Biomimetics, ROBIO 2019, Institute of Electrical and Electronics Engineers Inc., pp. 456-460, 2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019, Dali, China, 6/12/19. https://doi.org/10.1109/ROBIO49542.2019.8961438

Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles. / Chen, Hao; Xue, Chunyue; Liu, Shoubin et al.
IEEE International Conference on Robotics and Biomimetics, ROBIO 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 456-460 8961438 (IEEE International Conference on Robotics and Biomimetics, ROBIO 2019).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles

AU - Chen, Hao

AU - Xue, Chunyue

AU - Liu, Shoubin

AU - Sun, Yuxiang

AU - Chen, Yongquan

PY - 2019/12

Y1 - 2019/12

N2 - This article describes a multi-object tracking method through sensor fusion with a monocular camera and a 3-D Lidar for autonomous vehicles. Specifically, several pairwise costs from information, such as locations, movements, and poses of 3-D cues, are designed for tracking. These costs can complement each other to reduce matching errors during the tracking process. Moreover, they are efficient to be on-line computed with embedded equipment. We feed the pairwise costs to the data-association framework, which is based on the Hungarian algorithm, and then do the back-end fusion for the tracking results. The experimental results on our autonomous sightseeing car demonstrate that our tracking method could achieve accurate and robust results in real-world traffic scenarios.

AB - This article describes a multi-object tracking method through sensor fusion with a monocular camera and a 3-D Lidar for autonomous vehicles. Specifically, several pairwise costs from information, such as locations, movements, and poses of 3-D cues, are designed for tracking. These costs can complement each other to reduce matching errors during the tracking process. Moreover, they are efficient to be on-line computed with embedded equipment. We feed the pairwise costs to the data-association framework, which is based on the Hungarian algorithm, and then do the back-end fusion for the tracking results. The experimental results on our autonomous sightseeing car demonstrate that our tracking method could achieve accurate and robust results in real-world traffic scenarios.

UR - https://www.scopus.com/pages/publications/85079031427

U2 - 10.1109/ROBIO49542.2019.8961438

DO - 10.1109/ROBIO49542.2019.8961438

M3 - Conference Paper published in a book

AN - SCOPUS:85079031427

T3 - IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

SP - 456

EP - 460

BT - IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

Y2 - 6 December 2019 through 8 December 2019

ER -

Chen H, Xue C, Liu S, Sun Y, Chen Y. Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles. In IEEE International Conference on Robotics and Biomimetics, ROBIO 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 456-460. 8961438. (IEEE International Conference on Robotics and Biomimetics, ROBIO 2019). doi: 10.1109/ROBIO49542.2019.8961438

Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this