Moving target segmentation using Markov random field-based evaluation metric in infrared videos

Abstract

A method for moving target detection and segmentation using Markov random field (MRF)-based evaluation metric in infrared videos has been proposed. Starting with the most useful seeds of a moving object, which are extracted based on the “holes” effect of temporal difference; the proposed method employs a region growing method using local gray information and a spatial and temporal MRF model-based evaluation metric without ground truth for moving target segmentation in infrared videos. The segmented mask of a moving target is grown from the most useful seeds using the region growing method with thresholds. The proposed evaluation metric is utilized to determine the best growing threshold, where the performance of moving target segmentation is measured by that of segmented mask’s boundary. Thus, an MRF modeling for each boundary point of the segmented mask in spatial and temporal directions was considered by us. This problem is formulated using maximum a posteriori (MAP) estimation principle. At last, the global optimum of MRF-MAP framework is achieved using simulated annealing algorithm. The best segmented mask of a moving target is grown from the most useful seeds with the best growing threshold. Experimental results are reported to demonstrate the accuracy and robustness of our algorithm.