TY - GEN
T1 - A simplified minimum enclosing ball based fast incremental support vector machine (SVM) algorithm for person detection and tracking
AU - Zheng, Suiwu
AU - Qiao, Hong
AU - Jia, Lihao
AU - Fukuda, Toshio
PY - 2012
Y1 - 2012
N2 - In order to meet the requirements of stable person detection and tracking techniques in dynamic visual system, we propose a simplified minimum enclosing ball based fast incremental support vector machine (SVM) algorithm for person detection and tracking. Based on the simplified minimum enclosing ball (MEB) method, we propose a simplified and fast incremental algorithm to compute the MEB. By utilizing the equivalence between MEB and the dual problem in SVM, we achieve the online and incremental adjustment of the SVM classifier coefficients. The proposed method do not need to solve the quadratic programming problem. It is fast for training. Moreover, it can achieve the online update of classifiers for object tracking with small sample size. Finally, the efficiency of the proposed incremental SVM is validated by detection experiments on dynamic pedestrians tracking system.
AB - In order to meet the requirements of stable person detection and tracking techniques in dynamic visual system, we propose a simplified minimum enclosing ball based fast incremental support vector machine (SVM) algorithm for person detection and tracking. Based on the simplified minimum enclosing ball (MEB) method, we propose a simplified and fast incremental algorithm to compute the MEB. By utilizing the equivalence between MEB and the dual problem in SVM, we achieve the online and incremental adjustment of the SVM classifier coefficients. The proposed method do not need to solve the quadratic programming problem. It is fast for training. Moreover, it can achieve the online update of classifiers for object tracking with small sample size. Finally, the efficiency of the proposed incremental SVM is validated by detection experiments on dynamic pedestrians tracking system.
KW - Incremental Support Vector Machine
KW - Person Detection
UR - https://www.scopus.com/pages/publications/84872349584
U2 - 10.1109/WCICA.2012.6359413
DO - 10.1109/WCICA.2012.6359413
M3 - 会议稿件
AN - SCOPUS:84872349584
SN - 9781467313988
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 4936
EP - 4941
BT - WCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation
T2 - 10th World Congress on Intelligent Control and Automation, WCICA 2012
Y2 - 6 July 2012 through 8 July 2012
ER -