Distributed Algorithms for Probabilistic Solution of Computational Vision Problems

摘要

A new approach is developed for solving the moving target detection and tracking problem using highly cluttered images. The unknown target is assumed to be moving over a cluttered background in the presence of foreground noise. Using a Markov random field model for the target and a probabilistic description of the noise, the posterior distribution of the target is a Gibbs distribution. The maximum aposteriori target image is found by a randomized search process. Both batch and recursive formulations are developed, with the recursive approach yielding superior results. Numerical results indicate that this approach can successfully detect and track small targets in environments where the target is essentially made invisible by noise. The algorithms are almost completely parallelizable: for n pixels a total of n/4 processors may be used, with the result that solutions would require on the order of 2 seconds on current machines for the examples presented.