Resource-aware architectures for adaptive particle filter based visual target tracking

Abstract

There are a growing number of visual tracking applications now being envisioned for mobile devices. However, since computer vision algorithms such as particle filtering have large computational demands, they can result in high energy consumption and temperatures in mobile devices. Conventional approaches for distributed target tracking with a camera node and a receiver node are either sender-based (SB) or receiver-based (RB). The SB approach uses little energy and bandwidth, but requires a sender with large computational resources. The RB approach fits applications where computational resources are completely unavailable to the sender, but requires very large energy and bandwidth. In this article, we propose three architectures for distributed particle filtering that (i) reduce particle filtering workload and (ii) allow for dynamic migration of workload between nodes participating in tracking. We also discuss an adaptive particle filtering extension that adapts particle filter computational complexity and can be applied to both the conventional and proposed architectures for improved energy efficiency. Results show that the proposed solutions require low additional overhead, improve on tracking system lifetime, balance node temperatures, maintain track of the desired target, and are more effective than conventional approaches in many scenarios.