TY - JOUR
T1 - Real-time pedestrian detection using support vector machines
AU - Kang, Seonghoon
AU - Byun, Hyeran
AU - Lee, Seong Whan
N1 - Funding Information:
∗This research was supported Technology, Korea. †Author for correspondence.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2003/5
Y1 - 2003/5
N2 - In this paper, we present a real-time pedestrian detection method in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system for the visually impaired. It detects foreground objects on the ground, discriminates pedestrians from other noninterest objects, and extracts candidate regions for face detection and recognition. For effective real-time pedestrian detection, we have developed a method using stereo-based segmentation and the SVM (Support Vector Machines), which works well particularly in binary classification problem (e.g. object detection). We used vertical edge features extracted from arms, legs and torso. In our experiments, test results on a large number of outdoor scenes demonstrated the effectiveness of the proposed pedestrian detection method.
AB - In this paper, we present a real-time pedestrian detection method in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system for the visually impaired. It detects foreground objects on the ground, discriminates pedestrians from other noninterest objects, and extracts candidate regions for face detection and recognition. For effective real-time pedestrian detection, we have developed a method using stereo-based segmentation and the SVM (Support Vector Machines), which works well particularly in binary classification problem (e.g. object detection). We used vertical edge features extracted from arms, legs and torso. In our experiments, test results on a large number of outdoor scenes demonstrated the effectiveness of the proposed pedestrian detection method.
KW - Pedestrian detection
KW - Stereo vision
KW - Support vector machines
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U2 - 10.1142/S0218001403002435
DO - 10.1142/S0218001403002435
M3 - Article
AN - SCOPUS:0037591567
SN - 0218-0014
VL - 17
SP - 405
EP - 416
JO - International Journal of Pattern Recognition and Artificial Intelligence
JF - International Journal of Pattern Recognition and Artificial Intelligence
IS - 3
ER -