  Real-time visual analysis tasks, like tracking and recognition, require swift
execution of computationally intensive algorithms. Visual sensor networks can
be enabled to perform such tasks by augmenting the sensor network with
processing nodes and distributing the computational burden in a way that the
cameras contend for the processing nodes while trying to minimize their task
completion times. In this paper, we formulate the problem of minimizing the
completion time of all camera sensors as an optimization problem. We propose
algorithms for fully distributed optimization, analyze the existence of
equilibrium allocations, evaluate the effect of the network topology and of the
video characteristics, and the benefits of central coordination. Our results
demonstrate that with sufficient information available, distributed
optimization can provide low completion times, moreover predictable and stable
performance can be achieved with additional, sparse central coordination.
