TY - GEN
T1 - New efficient speed-up scheme for cascade implementation of SVM classifier
AU - Baek, Jeonghyun
AU - Kim, Jisu
AU - Hyun, Junhyuk
AU - Kim, Euntai
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2015/9/28
Y1 - 2015/9/28
N2 - For intelligent vehicle applications, detecting pedestrian technique must be robust and perform in real time. In pedestrian detection, support vector machine (SVM) is one of the popular classifiers because of its robust performance. In this paper, we propose the new method to implement cascade SVM that enables fast rejection of negative samples. The proposed method is tested with INRIA person dataset and show better rejection performance of negative samples than conventional method.
AB - For intelligent vehicle applications, detecting pedestrian technique must be robust and perform in real time. In pedestrian detection, support vector machine (SVM) is one of the popular classifiers because of its robust performance. In this paper, we propose the new method to implement cascade SVM that enables fast rejection of negative samples. The proposed method is tested with INRIA person dataset and show better rejection performance of negative samples than conventional method.
UR - http://www.scopus.com/inward/record.url?scp=84951206325&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951206325&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2015.7280810
DO - 10.1109/IJCNN.2015.7280810
M3 - Conference contribution
AN - SCOPUS:84951206325
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2015 International Joint Conference on Neural Networks, IJCNN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Joint Conference on Neural Networks, IJCNN 2015
Y2 - 12 July 2015 through 17 July 2015
ER -