As a natural and intuitive way of interpersonal communication, gestures are widely valued in the application of human-computer interaction. Especially, gesture recognition based the vision has such advantages as no equipment, high freedom and high robustness, but it will still be influenced by the factors such as light or environment, so it is an important research side to deal with the process of gesture recognition with some optimization algorithms. This paper analyzes the shortcomings of traditional gesture recognition, the great potential of solving complex optimization problems from genetic algorithm and the successful application in the field of industrial engineering. At the same time, combining the characteristics of the performance of gesture recognition system, this paper summarizes the research status and application of hand gesture recognition based on GA.
{"title":"Review","authors":"Ming-Chao Yu, Gongfa Li, Ying Sun, Bo Tao, Shuang Xu, Fei Zeng","doi":"10.1145/3316551.3318230","DOIUrl":"https://doi.org/10.1145/3316551.3318230","url":null,"abstract":"As a natural and intuitive way of interpersonal communication, gestures are widely valued in the application of human-computer interaction. Especially, gesture recognition based the vision has such advantages as no equipment, high freedom and high robustness, but it will still be influenced by the factors such as light or environment, so it is an important research side to deal with the process of gesture recognition with some optimization algorithms. This paper analyzes the shortcomings of traditional gesture recognition, the great potential of solving complex optimization problems from genetic algorithm and the successful application in the field of industrial engineering. At the same time, combining the characteristics of the performance of gesture recognition system, this paper summarizes the research status and application of hand gesture recognition based on GA.","PeriodicalId":110927,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Digital Signal Processing - ICDSP 2019","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123464543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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