Authors:
Yoshiki Tatebe
1
;
Daisuke Deguchi
1
;
Yasutomo Kawanishi
1
;
Ichiro Ide
1
;
Hiroshi Murase
1
and
Utsushi Sakai
2
Affiliations:
1
Nagoya University, Japan
;
2
DENSO CORPORATION, Japan
Keyword(s):
LIDAR, Pedestrian Detection, Low-resolution.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
Abstract:
In recent years, demand for pedestrian detection using inexpensive low-resolution LIDAR (LIght Detection
And Ranging) is increasing, as it can be used to prevent traffic accidents involving pedestrians. However, it is
difficult to detect pedestrians from a low-resolution (sparse) point-cloud obtained by a low-resolution LIDAR.
In this paper, we propose multi-frame features calculated by integrating point-clouds over multiple frames for
increasing the point-cloud resolution, and extracting their temporal changes. By combining these features,
the accuracy of the pedestrian detection from low-resolution point-clouds can be improved. We conducted
experiments using LIDAR data obtained in actual traffic environments. Experimental results showed that the
proposed method could detect pedestrians accurately from low-resolution LIDAR data.