Adaptable architectures for distributed visual target tracking

There are a growing number of visual tracking applications for mobile devices. However, the computer vision algorithms which process real-time video to track moving targets are demanding. Since a single mobile device possesses limited computational capabilities, energy, etc. to fully support target tracking, some works have investigated architectures which migrate a portion of tracking duties to another device at the cost of transmission bandwidth and energy. In this paper, we investigate the resource utilization in such architectures and present an adaptable architecture which balances tracking workload among the participating devices based on current resource availability (energy, temperature, bandwidth). Results show that the proposed solution requires low additional overhead, can improve on tracking system lifetime by reducing energy consumption, and is more effective in maintaining safe operating temperatures within participants as compared to previously investigated architecture.