Moving object detection using Markov Random Field and Distributed Differential Evolution

In this article, we present an algorithm for detecting moving objects from a given video sequence. Here, spatial and temporal segmentations are combined together to detect moving objects. In spatial segmentation, a multi-layer compound Markov Random Field (MRF) is used which models spatial, temporal, and edge attributes of image frames of a given video. Segmentation is viewed as a pixel labeling problem and is solved using the maximum a posteriori (MAP) probability estimation principle; i.e., segmentation is done by searching a labeled configuration that maximizes this probability. We have proposed using a Differential Evolution (DE) algorithm with neighborhood-based mutation (termed as Distributed Differential Evolution (DDE) algorithm) for estimating the MAP of the MRF model. A window is considered over the entire image lattice for mutation of each target vector of the DDE; thereby enhancing the speed of convergence. In case of temporal segmentation, the Change Detection Mask (CDM) is obtained bject detection by thresholding the absolute differences of the two consecutive spatially segmented image frames. The intensity/color values of the original pixels of the considered current frame are superimposed in the changed regions of the modified CDM to extract the Video Object Planes (VOPs). To test the effectiveness of the proposed algorithm, five reference and one real life video sequences are considered. Results of the proposed method are compared with four state of the art techniques and provide better spatial segmentation and better identification of the location of moving objects.