Resource-Aware Distributed Particle Filtering For Cluster-Based Object Tracking In Wireless Camera Networks

This paper presents a novel resource-aware framework for the implementation of distributed particle filters in resource-constrained wireless camera networks (WCNs). WCNs often suffer from communication failures caused by physical limitations of the communication channel as well as network congestion. Unreliable communication degrades the visual information shared by the cameras, such as visual feature data, and consequently leads to inaccurate vision processing at individual camera nodes. This paper focuses on the effects of communication failures on object tracking performance and presents a novel communication resource-aware tracking methodology, which adjusts the amount of data packets transmitted by the cameras according to the network conditions. We demonstrate the performance of the proposed framework using three different mechanisms to share the particle information among nodes: synchronised particles, Gaussian mixture models, and Parzen windows. The experimental results show that the proposed resource-aware method makes the distributed particle filters more tolerant to packet losses and also more energy efficient.