Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2015 International Joint Conference on Neural Networks, IJCNN 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Volume | 2015-September |
| ISBN (Electronic) | 9781479919604, 9781479919604, 9781479919604, 9781479919604 |
| DOIs | |
| Publication status | Published - 2015 Sep 28 |
| Event | International Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland Duration: 2015 Jul 12 → 2015 Jul 17 |
Other
| Other | International Joint Conference on Neural Networks, IJCNN 2015 |
|---|---|
| Country | Ireland |
| City | Killarney |
| Period | 15/7/12 → 15/7/17 |
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All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
Cite this
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New efficient speed-up scheme for cascade implementation of SVM classifier. / Baek, Jeonghyun; Kim, Jisu; Hyun, Junhyuk; Kim, Euntai.
2015 International Joint Conference on Neural Networks, IJCNN 2015. Vol. 2015-September Institute of Electrical and Electronics Engineers Inc., 2015. 7280810.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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
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
VL - 2015-September
BT - 2015 International Joint Conference on Neural Networks, IJCNN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -