Algorithms for distributed feature extraction in multi-camera visual sensor networks

Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Enabling visual sensor networks to perform such tasks can be achieved by augmenting the sensor network with processing nodes and distributing the computational burden among several nodes, in a way that the cameras contend for the processing nodes while trying to minimize their 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, and evaluate their performance. Simulation results show that distributed optimization can provide good performance despite limited information availability at low computational complexity, but the predictable and stable performance is often not provided by the algorithm that provides lowest average completion time.