Moving object detection using spatio-temporal multilayer compound Markov Random Field and histogram thresholding based change detection
Multimedia Tools and Applications
In this article, we propose a Multi Layer Compound Markov Random Field (MLCMRF) Model to spatially segment different image frames of a given video sequence. The segmented image frames are combined with the change between the frames to detect the moving objects from a video. The proposed MLCMRF uses five Markov models in a single framework, one in spatial direction using color feature, four in temporal direction (using two color features and two edges/line fields). Hence, the proposed MLCMRF is a combination of spatial distribution of color, temporal color coherence and edge maps in the temporal frames. The use of such an edge preserving model helps in enhancing the object boundary in spatial segmentation and hence can detect moving objects with less effect of silhouette. A difference between the frames is used to generate the CDM and is subsequently updated with the previous frame video object plane (VOP) and the spatial segmentation of the consecutive frames, to detect the moving objects from the target image frames. Results of the proposed spatial segmentation approach are compared with those of the existing state-of-the-art techniques and are found to be better.
Subudhi, Badri Narayan; Ghosh, Susmita; Nanda, Pradipta Kumar; and Ghosh, Ashish, "Moving object detection using spatio-temporal multilayer compound Markov Random Field and histogram thresholding based change detection" (2017). Journal Articles. 2561.