Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection

Seong Kyung Kwon; S. H. Son; Eugin Hyun; Jin Hee Lee; Jonghun Lee

doi:10.1109/CSCI.2017.322

Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection

Seong Kyung Kwon
, S. H. Son
, Eugin Hyun
, Jin Hee Lee
, Jonghun Lee

Daegu Gyeongbuk Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

Despite the development of sensors and their sensor fusion technologies, pedestrian detection technology is a still challenging topic. The pedestrian detection using LIDAR-RADAR fusion method hasn¡t yet been reported. We propose the occluded depth generation based LIDAR-RADAR sensor fusion scheme. The proposed method consists of object detection, occluded depth generation and then pedestrian detection. Objects within the occluded depth are detected by RADAR and an occluded object is estimated to a pedestrian by means of RADAR human Doppler distribution.

Original language	English
Title of host publication	Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
Editors	Fernando G. Tinetti, Quoc-Nam Tran, Leonidas Deligiannidis, Mary Qu Yang, Mary Qu Yang, Hamid R. Arabnia
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1811-1812
Number of pages	2
ISBN (Electronic)	9781538626528
DOIs	https://doi.org/10.1109/CSCI.2017.322
State	Published - 4 Dec 2018
Event	2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 - Las Vegas, United States Duration: 14 Dec 2017 → 16 Dec 2017

Publication series

Name	Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

Conference

Conference	2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
Country/Territory	United States
City	Las Vegas
Period	14/12/17 → 16/12/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Radar
active safety system
detection
lidar
pedestrian
sensor fusion

Access to Document

10.1109/CSCI.2017.322

Cite this

Kwon, S. K., Son, S. H., Hyun, E., Lee, J. H., & Lee, J. (2018). Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection. In F. G. Tinetti, Q.-N. Tran, L. Deligiannidis, M. Q. Yang, M. Q. Yang, & H. R. Arabnia (Eds.), Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 (pp. 1811-1812). Article 8561087 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI.2017.322

Kwon, Seong Kyung ; Son, S. H. ; Hyun, Eugin et al. / Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection. Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. editor / Fernando G. Tinetti ; Quoc-Nam Tran ; Leonidas Deligiannidis ; Mary Qu Yang ; Mary Qu Yang ; Hamid R. Arabnia. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1811-1812 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017).

@inproceedings{4fd4e2a7b6d54f6daa7140724e636228,

title = "Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection",

abstract = "Despite the development of sensors and their sensor fusion technologies, pedestrian detection technology is a still challenging topic. The pedestrian detection using LIDAR-RADAR fusion method hasn¡t yet been reported. We propose the occluded depth generation based LIDAR-RADAR sensor fusion scheme. The proposed method consists of object detection, occluded depth generation and then pedestrian detection. Objects within the occluded depth are detected by RADAR and an occluded object is estimated to a pedestrian by means of RADAR human Doppler distribution.",

keywords = "Radar, active safety system, detection, lidar, pedestrian, sensor fusion",

author = "Kwon, \{Seong Kyung\} and Son, \{S. H.\} and Eugin Hyun and Lee, \{Jin Hee\} and Jonghun Lee",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 ; Conference date: 14-12-2017 Through 16-12-2017",

year = "2018",

month = dec,

day = "4",

doi = "10.1109/CSCI.2017.322",

language = "English",

series = "Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017",

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

pages = "1811--1812",

editor = "Tinetti, \{Fernando G.\} and Quoc-Nam Tran and Leonidas Deligiannidis and Yang, \{Mary Qu\} and Yang, \{Mary Qu\} and Arabnia, \{Hamid R.\}",

booktitle = "Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017",

}

Kwon, SK, Son, SH , Hyun, E, Lee, JH & Lee, J 2018, Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection. in FG Tinetti, Q-N Tran, L Deligiannidis, MQ Yang, MQ Yang & HR Arabnia (eds), Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017., 8561087, Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 1811-1812, 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, Las Vegas, United States, 14/12/17. https://doi.org/10.1109/CSCI.2017.322

Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection. / Kwon, Seong Kyung; Son, S. H.; Hyun, Eugin et al.
Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. ed. / Fernando G. Tinetti; Quoc-Nam Tran; Leonidas Deligiannidis; Mary Qu Yang; Mary Qu Yang; Hamid R. Arabnia. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1811-1812 8561087 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection

AU - Kwon, Seong Kyung

AU - Son, S. H.

AU - Hyun, Eugin

AU - Lee, Jin Hee

AU - Lee, Jonghun

PY - 2018/12/4

Y1 - 2018/12/4

N2 - Despite the development of sensors and their sensor fusion technologies, pedestrian detection technology is a still challenging topic. The pedestrian detection using LIDAR-RADAR fusion method hasn¡t yet been reported. We propose the occluded depth generation based LIDAR-RADAR sensor fusion scheme. The proposed method consists of object detection, occluded depth generation and then pedestrian detection. Objects within the occluded depth are detected by RADAR and an occluded object is estimated to a pedestrian by means of RADAR human Doppler distribution.

AB - Despite the development of sensors and their sensor fusion technologies, pedestrian detection technology is a still challenging topic. The pedestrian detection using LIDAR-RADAR fusion method hasn¡t yet been reported. We propose the occluded depth generation based LIDAR-RADAR sensor fusion scheme. The proposed method consists of object detection, occluded depth generation and then pedestrian detection. Objects within the occluded depth are detected by RADAR and an occluded object is estimated to a pedestrian by means of RADAR human Doppler distribution.

KW - Radar

KW - active safety system

KW - detection

KW - lidar

KW - pedestrian

KW - sensor fusion

UR - https://www.scopus.com/pages/publications/85060621719

U2 - 10.1109/CSCI.2017.322

DO - 10.1109/CSCI.2017.322

M3 - Conference contribution

AN - SCOPUS:85060621719

T3 - Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

SP - 1811

EP - 1812

BT - Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

A2 - Tinetti, Fernando G.

A2 - Tran, Quoc-Nam

A2 - Deligiannidis, Leonidas

A2 - Yang, Mary Qu

A2 - Arabnia, Hamid R.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

Y2 - 14 December 2017 through 16 December 2017

ER -

Kwon SK, Son SH , Hyun E, Lee JH, Lee J. Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection. In Tinetti FG, Tran QN, Deligiannidis L, Yang MQ, Yang MQ, Arabnia HR, editors, Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1811-1812. 8561087. (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017). doi: 10.1109/CSCI.2017.322

Radar-Lidar Sensor Fusion Sheme Using Occluded Depth Generation for Pedestrian Detection

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this