{"title":"基于时频分析的步态识别","authors":"Xiaxi Huang, N. Boulgouris, A. Georgakis","doi":"10.1109/ICDSP.2009.5201264","DOIUrl":null,"url":null,"abstract":"In this paper, we extract model-based gait features and investigate the time-frequency representations of the feature signals. A novel gait recognition approach is proposed, which is based on time-frequency analysis of gait feature signals using the Wigner distribution. Time-frequency analysis using theWigner distribution is aimed at capturing gait information that is not extractable using other time-domain or frequency-domain techniques. Experiments, conducted based on the above approach, yielded encouraging results.","PeriodicalId":409669,"journal":{"name":"2009 16th International Conference on Digital Signal Processing","volume":"619 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Gait recognition based on time-frequency analysis\",\"authors\":\"Xiaxi Huang, N. Boulgouris, A. Georgakis\",\"doi\":\"10.1109/ICDSP.2009.5201264\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we extract model-based gait features and investigate the time-frequency representations of the feature signals. A novel gait recognition approach is proposed, which is based on time-frequency analysis of gait feature signals using the Wigner distribution. Time-frequency analysis using theWigner distribution is aimed at capturing gait information that is not extractable using other time-domain or frequency-domain techniques. Experiments, conducted based on the above approach, yielded encouraging results.\",\"PeriodicalId\":409669,\"journal\":{\"name\":\"2009 16th International Conference on Digital Signal Processing\",\"volume\":\"619 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 16th International Conference on Digital Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDSP.2009.5201264\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 16th International Conference on Digital Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDSP.2009.5201264","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we extract model-based gait features and investigate the time-frequency representations of the feature signals. A novel gait recognition approach is proposed, which is based on time-frequency analysis of gait feature signals using the Wigner distribution. Time-frequency analysis using theWigner distribution is aimed at capturing gait information that is not extractable using other time-domain or frequency-domain techniques. Experiments, conducted based on the above approach, yielded encouraging results.