Co-evolution based feature selection for pedestrian detection
Guo, Y.P.; Cao, X.B.; Xu, Y.W.; Hong, Q.
2007
Conference Name2007 IEEE International Conference on Control and Automation, ICCA 2007
Source Publication2007 IEEE International Conference on Control and Automation, ICCA 2007
Conference Date30 May-1 June 2007
Conference PlaceGuangzhou, China
AbstractIn 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.
KeywordAutomated Highways / Object Detection / Road Safety / Adaboost Algorithm / Coevolution Based Feature Selection / Pedestrian Detection System / Automatic Control / Automation / Communication System Software / Computer Science
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12823
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorGuo, Y.P.
AffiliationDept. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China
Recommended Citation
GB/T 7714
Guo, Y.P.,Cao, X.B.,Xu, Y.W.,et al. Co-evolution based feature selection for pedestrian detection[C],2007.
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