{"title":"分布式异构传感器网络中基于主动和被动检测信息的单目标跟踪","authors":"Hyunhak Shin, C. Cho, Hanseok Ko","doi":"10.1109/AVSS.2016.7738083","DOIUrl":null,"url":null,"abstract":"In this paper, a single object tracking method based on fusion of detection information collected from a distributed heterogeneous sensor network is proposed. The considered sensor network is composed of one active type source and multiple receivers. It is assumed that the heterogeneous network is capable of acquiring both passive and active information simultaneously. By means of fusion of the acquired heterogeneous data, the proposed method estimates the candidate region of target location. Then, position of the object is estimated by Maximum Likelihood Estimation. In the experimental results, the performance of the proposed method is demonstrated in terms of deployment strategy of the heterogeneous sensor network.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Single object tracking based on active and passive detection information in distributed heterogeneous sensor network\",\"authors\":\"Hyunhak Shin, C. Cho, Hanseok Ko\",\"doi\":\"10.1109/AVSS.2016.7738083\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a single object tracking method based on fusion of detection information collected from a distributed heterogeneous sensor network is proposed. The considered sensor network is composed of one active type source and multiple receivers. It is assumed that the heterogeneous network is capable of acquiring both passive and active information simultaneously. By means of fusion of the acquired heterogeneous data, the proposed method estimates the candidate region of target location. Then, position of the object is estimated by Maximum Likelihood Estimation. In the experimental results, the performance of the proposed method is demonstrated in terms of deployment strategy of the heterogeneous sensor network.\",\"PeriodicalId\":438290,\"journal\":{\"name\":\"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AVSS.2016.7738083\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AVSS.2016.7738083","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Single object tracking based on active and passive detection information in distributed heterogeneous sensor network
In this paper, a single object tracking method based on fusion of detection information collected from a distributed heterogeneous sensor network is proposed. The considered sensor network is composed of one active type source and multiple receivers. It is assumed that the heterogeneous network is capable of acquiring both passive and active information simultaneously. By means of fusion of the acquired heterogeneous data, the proposed method estimates the candidate region of target location. Then, position of the object is estimated by Maximum Likelihood Estimation. In the experimental results, the performance of the proposed method is demonstrated in terms of deployment strategy of the heterogeneous sensor network.