Vision-Based People Detection System for Heavy Machine Applications
AbstractThis 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
Scifeed alert for new publicationsNever 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
Fremont, V.; Bui, M.T.; Boukerroui, D.; Letort, P. Vision-Based People Detection System for Heavy Machine Applications. Sensors 2016, 16, 128.
Fremont V, Bui MT, Boukerroui D, Letort P. Vision-Based People Detection System for Heavy Machine Applications. Sensors. 2016; 16(1):128.Chicago/Turabian Style
Fremont, Vincent; Bui, Manh T.; Boukerroui, Djamal; Letort, Pierrick. 2016. "Vision-Based People Detection System for Heavy Machine Applications." Sensors 16, no. 1: 128.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.