Pedestrian detection using triple laser range finders

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low number of laser data were obtained.

Original languageEnglish
Pages (from-to)3037-3045
Number of pages9
JournalInternational Journal of Electrical and Computer Engineering
Volume7
Issue number6
DOIs
Publication statusPublished - 1 Dec 2017

Fingerprint

Range finders
Lasers
Fusion reactions
Laser fusion
Ground vehicles
Feature extraction
Navigation
Experiments

Keywords

  • Autonomous
  • Laser Range Finder
  • Pedestrian Detection

ASJC Scopus subject areas

  • Computer Science(all)
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

@article{67c8677c6024421e83382a08ef7c3534,
title = "Pedestrian detection using triple laser range finders",
abstract = "Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low number of laser data were obtained.",
keywords = "Autonomous, Laser Range Finder, Pedestrian Detection",
author = "{Abd Rahman }, {Abdul Hadi} and {Zainol Ariffin }, {Khairul Akram} and Sani, {Nor Samsiah} and Hairi Zamzuri",
year = "2017",
month = "12",
day = "1",
doi = "10.11591/ijece.v7i6.pp3037-3045",
language = "English",
volume = "7",
pages = "3037--3045",
journal = "International Journal of Electrical and Computer Engineering",
issn = "2088-8708",
publisher = "Institute of Advanced Engineering and Science (IAES)",
number = "6",

}

TY - JOUR

T1 - Pedestrian detection using triple laser range finders

AU - Abd Rahman , Abdul Hadi

AU - Zainol Ariffin , Khairul Akram

AU - Sani, Nor Samsiah

AU - Zamzuri, Hairi

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low number of laser data were obtained.

AB - Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low number of laser data were obtained.

KW - Autonomous

KW - Laser Range Finder

KW - Pedestrian Detection

UR - http://www.scopus.com/inward/record.url?scp=85032227667&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032227667&partnerID=8YFLogxK

U2 - 10.11591/ijece.v7i6.pp3037-3045

DO - 10.11591/ijece.v7i6.pp3037-3045

M3 - Article

AN - SCOPUS:85032227667

VL - 7

SP - 3037

EP - 3045

JO - International Journal of Electrical and Computer Engineering

JF - International Journal of Electrical and Computer Engineering

SN - 2088-8708

IS - 6

ER -