Cooperative features extraction in visual sensor networks: a game-theoretic approach

Abstract

Visual Sensor Networks consist of several camera nodes that perform analysis tasks, such as object recognition. In many cases camera nodes have overlapping fields of view. Such overlap is typically leveraged in two different ways: (i) to improve the accuracy/quality of the visual analysis task by exploiting multi-view information or (ii) to reduce the consumed energy by applying temporal scheduling techniques among the multiple cameras. In this work, we propose a game theoretic framework based Nash Bargaining Solution to bridge the gap between the two aforementioned approaches. The key tenet of the proposed framework is for cameras to reduce the consumed energy in the analysis process by exploiting the redundancy in the reciprocal fields of view. Experimental results confirm that the proposed scheme is able to improve the network lifetime, with a negligible loss in terms of visual analysis accuracy.