{"title":"视觉显著性提高自主视觉搜索","authors":"Amir Rasouli, John K. Tsotsos","doi":"10.1109/CRV.2014.23","DOIUrl":null,"url":null,"abstract":"Visual search for a specific object in an unknown environment by autonomous robots is a complex task. The key challenge is to locate the object of interest while minimizing the cost of search in terms of time or energy consumption. Given the impracticality of examining all possible views of the search environment, recent studies suggest the use of attentive processes to optimize visual search. In this paper, we describe a method of visual search that exploits the use of attention in the form of a saliency map. This map is used to update the probability distribution of which areas to examine next, increasing the utility of spatial volumes where objects consistent with the target's visual saliency are observed. We present experimental results on a mobile robot and conclude that our method improves the process of visual search in terms of reducing the time and number of actions to be performed to complete the process.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Visual Saliency Improves Autonomous Visual Search\",\"authors\":\"Amir Rasouli, John K. Tsotsos\",\"doi\":\"10.1109/CRV.2014.23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Visual search for a specific object in an unknown environment by autonomous robots is a complex task. The key challenge is to locate the object of interest while minimizing the cost of search in terms of time or energy consumption. Given the impracticality of examining all possible views of the search environment, recent studies suggest the use of attentive processes to optimize visual search. In this paper, we describe a method of visual search that exploits the use of attention in the form of a saliency map. This map is used to update the probability distribution of which areas to examine next, increasing the utility of spatial volumes where objects consistent with the target's visual saliency are observed. We present experimental results on a mobile robot and conclude that our method improves the process of visual search in terms of reducing the time and number of actions to be performed to complete the process.\",\"PeriodicalId\":385422,\"journal\":{\"name\":\"2014 Canadian Conference on Computer and Robot Vision\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Canadian Conference on Computer and Robot Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CRV.2014.23\",\"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 Canadian Conference on Computer and Robot Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRV.2014.23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visual search for a specific object in an unknown environment by autonomous robots is a complex task. The key challenge is to locate the object of interest while minimizing the cost of search in terms of time or energy consumption. Given the impracticality of examining all possible views of the search environment, recent studies suggest the use of attentive processes to optimize visual search. In this paper, we describe a method of visual search that exploits the use of attention in the form of a saliency map. This map is used to update the probability distribution of which areas to examine next, increasing the utility of spatial volumes where objects consistent with the target's visual saliency are observed. We present experimental results on a mobile robot and conclude that our method improves the process of visual search in terms of reducing the time and number of actions to be performed to complete the process.