多摄像机网络任务分配的加权赋权流行匹配

Lin Cui, W. Jia
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引用次数: 2

摘要

多摄像机网络(MCN)在当今社会需求和日常生活中变得越来越重要,每个摄像机都运行着面向应用的多任务,如视频监控、目标跟踪和定位等。MCN的最终目标是最好地满足用户对这些任务的偏好/期望,这是以前的工作没有很好地解决的问题。本文针对这一挑战,提出了一种新的多任务分配(PMT)加权赋权流行匹配问题,并提出了求解该问题的有效算法。用流行度来表示任务-摄像机匹配的最优性,我们可以找到一种匹配,其中任务分配到相应摄像机的次数最多,最接近任务的偏好。通过大量的仿真,我们证明了与那些基线方法相比,我们的方法可以有效地匹配到所有任务的满意度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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