ROBUST FOREGROUND DETECTION METHOD BASED ON MULTI-VIEW LEARNING

摘要

Provided is a robust foreground detection method based on multi-view learning, comprising: acquiring a reference background image from an input video by means of a time domain median filtering method, and performing iterative search and multi-scale integration on a current image and the reference background image, so as to acquire heterogeneous features; calculating the conditional probability density of a foreground type and the conditional probability density of a background type using the conditional independence of the heterogeneous features, and calculating the posterior probability of a foreground and the posterior probability of a background using a Bayesean rule according to a foreground likelihood, a background likelihood and a priori probability; and constructing an energy function of a Markov random field model by means of the posterior probability of the foreground, the posterior probability of the background and a time-space consistency constraint, and minimizing the energy function using a belief propagation algorithm, so as to obtain a segmentation result of the foreground and the background. The present invention can realize robust foreground detection in a complex challenging environment.