Unsupervised evaluation method using Markov random field for moving object segmentation in infrared videos

An unsupervised method is proposed for performance evaluation of the moving object segmentation using Markov random field (MRF) in infrared videos. This method focuses on the edge features and takes spatio-temporal information into account. The authors consider an MRF model for each edge point of a segmentation mask in spatial and temporal directions. This problem is then formulated using maximum a posteriori estimation principle to form a criterion of evaluation. Subjective evaluation is applied to measure the performance of the evaluation methods. The results show that the proposed method is superior to other unsupervised measures.