{"title":"基于智能手机的便携式实时场景识别系统","authors":"Zhenwen Gui","doi":"10.1109/UEMCON.2017.8248969","DOIUrl":null,"url":null,"abstract":"In the context of mapping and mobile navigation services, the recognition of the urban infrastructure plays an important role. However, majority of the existing recognition methods cannot be effectively run on the smartphones due to their large computational costs and high storages. In this paper, a fast scene recognition approach is introduced by fusing the outputs of inertial sensors and cameras of smartphones for real-time applications in large scenes. The proposed algorithm possesses the virtues of implementation easiness, storage efficiency and computation significance. Experimental results on the datasets of outdoor scenes and UKBench datasets demonstrate the effectiveness and robustness of the proposed algorithm.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"437 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A portable real-time scene recognition system on smartphone\",\"authors\":\"Zhenwen Gui\",\"doi\":\"10.1109/UEMCON.2017.8248969\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the context of mapping and mobile navigation services, the recognition of the urban infrastructure plays an important role. However, majority of the existing recognition methods cannot be effectively run on the smartphones due to their large computational costs and high storages. In this paper, a fast scene recognition approach is introduced by fusing the outputs of inertial sensors and cameras of smartphones for real-time applications in large scenes. The proposed algorithm possesses the virtues of implementation easiness, storage efficiency and computation significance. Experimental results on the datasets of outdoor scenes and UKBench datasets demonstrate the effectiveness and robustness of the proposed algorithm.\",\"PeriodicalId\":403890,\"journal\":{\"name\":\"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)\",\"volume\":\"437 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UEMCON.2017.8248969\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UEMCON.2017.8248969","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A portable real-time scene recognition system on smartphone
In the context of mapping and mobile navigation services, the recognition of the urban infrastructure plays an important role. However, majority of the existing recognition methods cannot be effectively run on the smartphones due to their large computational costs and high storages. In this paper, a fast scene recognition approach is introduced by fusing the outputs of inertial sensors and cameras of smartphones for real-time applications in large scenes. The proposed algorithm possesses the virtues of implementation easiness, storage efficiency and computation significance. Experimental results on the datasets of outdoor scenes and UKBench datasets demonstrate the effectiveness and robustness of the proposed algorithm.