@inproceedings{ea4e5728f70d4a83aaf0abb87324232a,
title = "Co-evolution based feature selection for pedestrian detection",
abstract = "In a pedestrian detection system, the most critical requirement is to quickly and reliably determine whether a candidate region contains a pedestrian. The detection ability of whole system determines directly upon quality of chosen features. However, due to the large number and various types of available features, it is difficult to find an optimal feature subset and acquire the proper feature proportion at the same time for most traditional methods including AdaBoost Algorithm. This paper presents a co-evolutionary method with sub-population size adjusting strategy for the feature selection problem in pedestrian detection system. Our method is able to find an optimal feature subset and adjust feature proportion to a proper state in the mean time. Experiments show that our method performs better than AdaBoost Algorithm.",
author = "Guo, \{Y. P.\} and Cao, \{X. B.\} and Xu, \{Y. W.\} and Q. Hong",
year = "2007",
doi = "10.1109/ICCA.2007.4376871",
language = "英语",
isbn = "1424408180",
series = "2007 IEEE International Conference on Control and Automation, ICCA",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2797--2801",
booktitle = "2007 IEEE International Conference on Control and Automation, ICCA",
note = "2007 IEEE International Conference on Control and Automation, ICCA ; Conference date: 30-05-2007 Through 01-06-2007",
}