Authors:
Daichi Suzuo
1
;
Daisuke Deguchi
1
;
Ichiro Ide
1
;
Hiroshi Murase
1
;
Hiroyuki Ishida
2
and
Yoshiko Kojima
3
Affiliations:
1
Nagoya University, Japan
;
2
Toyota Central Research and Development Laboratories and Inc., Japan
;
3
Toyota Central Research and Development Laboratories, Japan
Keyword(s):
Pedestrian Detection, ITS, Semi-supervised Learning.
Abstract:
In recent years, accurate pedestrian detection from in-vehicle camera images is focused to develop a safety driving assistance system. Currently, successful methods are based on statistical learning. However, in such methods, it is necessary to prepare a large amount of training images. Thus, the decrease in the number of training images degrades the detection accuracy. That is, in driving environments with few or no training images, it is difficult to detect pedestrians accurately. Therefore, we propose an approach that collects training images automatically to build classifiers for various driving environments. This is expected to realize highly accurate pedestrian detection by using an appropriate classifier corresponding to the current location. The proposed method consists of three steps; Classification of driving scenes, collection of non-pedestrian images and training of classifiers for each scene class, and associating a scene-class-specific classifier with GPS location infor
mation. Through experiments, we confirmed the effectiveness of the method compared to baseline methods.
(More)