{"title":"Human behavior detection method with direction change invariant features","authors":"Takeyuki Ishii, H. Murakami, A. Koike","doi":"10.1109/ISMICT.2013.6521740","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a new feature homogenization method using cubic higher-order local auto-correlation (CHLAC) to detect changes in human behavior. Conventional human behavior detection using CHLAC exhibits a high level of performance, but has difficulty in distinguishing between abnormal and normal movement. We propose a method with improved handling and statistical processing of mask patterns to suppress the change in the amount of features according to the direction of movement of the person. This provides a robust method of detecting changes in direction. A computer simulation using the proposed method demonstrates a superior performance composed to a conventional method in the recognition of abnormal human behavior.","PeriodicalId":387991,"journal":{"name":"2013 7th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 7th International Symposium on Medical Information and Communication Technology (ISMICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISMICT.2013.6521740","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we propose a new feature homogenization method using cubic higher-order local auto-correlation (CHLAC) to detect changes in human behavior. Conventional human behavior detection using CHLAC exhibits a high level of performance, but has difficulty in distinguishing between abnormal and normal movement. We propose a method with improved handling and statistical processing of mask patterns to suppress the change in the amount of features according to the direction of movement of the person. This provides a robust method of detecting changes in direction. A computer simulation using the proposed method demonstrates a superior performance composed to a conventional method in the recognition of abnormal human behavior.