{"title":"基于l1 -范数的LDA快速人体检测","authors":"Xiao Pu, Xiaoshuang Shi, Zhenhua Guo, Jie Zhou","doi":"10.1109/ICSS.2014.31","DOIUrl":null,"url":null,"abstract":"Fast detection is of vital importance in human detection sometimes. Considering the high dimensions of the features widely used in human detection, it will severely slow the detection speed. Therefore, in this paper, we try to find a way by using Linear Discriminant Analysis(LDA) via L1-norm regularization to solve this problem. It reduces the dimension of feature from 3780 to 150 before classification, and gets a more fast speed then SVM and LDA, while keeps a competitive accuracy.","PeriodicalId":206490,"journal":{"name":"2014 International Conference on Service Sciences","volume":"46 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Fast Human Detection Using LDA via L1-Norm\",\"authors\":\"Xiao Pu, Xiaoshuang Shi, Zhenhua Guo, Jie Zhou\",\"doi\":\"10.1109/ICSS.2014.31\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fast detection is of vital importance in human detection sometimes. Considering the high dimensions of the features widely used in human detection, it will severely slow the detection speed. Therefore, in this paper, we try to find a way by using Linear Discriminant Analysis(LDA) via L1-norm regularization to solve this problem. It reduces the dimension of feature from 3780 to 150 before classification, and gets a more fast speed then SVM and LDA, while keeps a competitive accuracy.\",\"PeriodicalId\":206490,\"journal\":{\"name\":\"2014 International Conference on Service Sciences\",\"volume\":\"46 5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Service Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSS.2014.31\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Service Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSS.2014.31","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast detection is of vital importance in human detection sometimes. Considering the high dimensions of the features widely used in human detection, it will severely slow the detection speed. Therefore, in this paper, we try to find a way by using Linear Discriminant Analysis(LDA) via L1-norm regularization to solve this problem. It reduces the dimension of feature from 3780 to 150 before classification, and gets a more fast speed then SVM and LDA, while keeps a competitive accuracy.
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