Next Article in Journal
Intravehicular, Short- and Long-Range Communication Information Fusion for Providing Safe Speed Warnings
Previous Article in Journal
EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(1), 128; doi:10.3390/s16010128

Vision-Based People Detection System for Heavy Machine Applications

1
Sorbonne Universités, Université de Technologie de Compiègne, CNRS, UMR 7253, Heudiasyc-CS 60 319, 60 203 Compiègne Cedex, France
2
Technical Center for the Mechanical Industry (CETIM), 60300 Senlis, France
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 12 October 2015 / Revised: 10 January 2016 / Accepted: 13 January 2016 / Published: 20 January 2016
(This article belongs to the Section Physical Sensors)

Abstract

This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance. View Full-Text
Keywords: heavy machines; sensor fusion; pedestrian detection; deformable part model; fisheye images; histogram of oriented gradients heavy machines; sensor fusion; pedestrian detection; deformable part model; fisheye images; histogram of oriented gradients
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Fremont, V.; Bui, M.T.; Boukerroui, D.; Letort, P. Vision-Based People Detection System for Heavy Machine Applications. Sensors 2016, 16, 128.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top