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.