{"title":"多摄像机网络任务分配的加权赋权流行匹配","authors":"Lin Cui, W. Jia","doi":"10.1109/ITC.2013.6662967","DOIUrl":null,"url":null,"abstract":"Multi-Camera Networks (MCN) are becoming increasingly important in today's society needs and daily-life with application-oriented multiple tasks running in each camera such as video surveillance, object tracking and localization etc. The ultimate goal of MCN is to best satisfy such tasks' preferences/expectations required by users, which has not been well-addressed by previous works. This paper investigates such challenge by formulating a novel weighted capacitated Popular Matching for multi-Task assignments (PMT) problem and proposing efficient algorithms to solve the problem. Using the popularity to represent the optimality of task-camera matching, we can find a matching in which the allocation of the most tasks to the corresponding cameras is closest to the tasks' preferences. With extensive simulations, we demonstrate that our approaches can make matching to the satisfaction of all tasks efficiently as compared to those baseline approaches.","PeriodicalId":252757,"journal":{"name":"Proceedings of the 2013 25th International Teletraffic Congress (ITC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Weighted capacitated Popular Matching for task assignment in Multi-Camera Networks\",\"authors\":\"Lin Cui, W. Jia\",\"doi\":\"10.1109/ITC.2013.6662967\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-Camera Networks (MCN) are becoming increasingly important in today's society needs and daily-life with application-oriented multiple tasks running in each camera such as video surveillance, object tracking and localization etc. The ultimate goal of MCN is to best satisfy such tasks' preferences/expectations required by users, which has not been well-addressed by previous works. This paper investigates such challenge by formulating a novel weighted capacitated Popular Matching for multi-Task assignments (PMT) problem and proposing efficient algorithms to solve the problem. Using the popularity to represent the optimality of task-camera matching, we can find a matching in which the allocation of the most tasks to the corresponding cameras is closest to the tasks' preferences. With extensive simulations, we demonstrate that our approaches can make matching to the satisfaction of all tasks efficiently as compared to those baseline approaches.\",\"PeriodicalId\":252757,\"journal\":{\"name\":\"Proceedings of the 2013 25th International Teletraffic Congress (ITC)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2013 25th International Teletraffic Congress (ITC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITC.2013.6662967\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2013 25th International Teletraffic Congress (ITC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITC.2013.6662967","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Weighted capacitated Popular Matching for task assignment in Multi-Camera Networks
Multi-Camera Networks (MCN) are becoming increasingly important in today's society needs and daily-life with application-oriented multiple tasks running in each camera such as video surveillance, object tracking and localization etc. The ultimate goal of MCN is to best satisfy such tasks' preferences/expectations required by users, which has not been well-addressed by previous works. This paper investigates such challenge by formulating a novel weighted capacitated Popular Matching for multi-Task assignments (PMT) problem and proposing efficient algorithms to solve the problem. Using the popularity to represent the optimality of task-camera matching, we can find a matching in which the allocation of the most tasks to the corresponding cameras is closest to the tasks' preferences. With extensive simulations, we demonstrate that our approaches can make matching to the satisfaction of all tasks efficiently as compared to those baseline approaches.