TY - GEN
T1 - Fast vehicle detection based on feature and real-time prediction
AU - Xu, Hanyang
AU - Zhou, Zhen
AU - Sheng, Bin
AU - Ma, Lizhuang
PY - 2013
Y1 - 2013
N2 - The vehicle identification is a key technology of vehicle automatic driving and assistance systems. This paper proposes a new fast vehicle detection method based on feature learning and real-time prediction by combining ARMA model and AdaBoost algorithm, which can be applied in car driver assistance systems for road detection and vehicle identification with a monocular camera. Experimental results show that our proposed algorithm can take the target's prior information into account, and extend AdaBoost algorithm in the time dimension that improve the accuracy of real-time detection to be faster and more accurate than the existing methods.
AB - The vehicle identification is a key technology of vehicle automatic driving and assistance systems. This paper proposes a new fast vehicle detection method based on feature learning and real-time prediction by combining ARMA model and AdaBoost algorithm, which can be applied in car driver assistance systems for road detection and vehicle identification with a monocular camera. Experimental results show that our proposed algorithm can take the target's prior information into account, and extend AdaBoost algorithm in the time dimension that improve the accuracy of real-time detection to be faster and more accurate than the existing methods.
UR - https://www.scopus.com/pages/publications/84883385738
U2 - 10.1109/ISCAS.2013.6572475
DO - 10.1109/ISCAS.2013.6572475
M3 - 会议稿件
AN - SCOPUS:84883385738
SN - 9781467357609
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 2860
EP - 2863
BT - 2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
T2 - 2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
Y2 - 19 May 2013 through 23 May 2013
ER -