Distributed differential evolution algorithm for MAP estimation of MRF model for detecting moving objects

In this article, spatio-temporal spatial and temporal segmentations are combined together to detect moving objects. In spatio-temporal spatial segmentation, a compound Markov Random Field (MRF) is used for modeling the image frames. Segmentation is viewed as a pixel labeling problem and is solved using Maximum a Posteriori (MAP) probability estimation principle; i.e., segmentation is achieved by searching a labeled configuration that maximizes this probability. To estimate the MAP of the MRF model, we have proposed a new Distributed Differential Evolution (DDE) algorithm where a small window is considered over the entire image lattice for mutation of each target vector of the conventional Differential Evolution (DE) algorithm. In temporal segmentation, the given video image frame is segmented into changed and unchanged regions by thresholding the absolute difference of two consecutive spatially segmented image frames. Thereafter Video Object Plane (VOP) is extracted by superimposing the intensity/ color values of original pixels of the current frame on the changed region. To test the effectiveness of the proposed algorithm, one reference video sequence is considered and results are found to be encouraging.