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The objective of this paper is to develop an approach for efficiently and quantitatively evaluating thresholding algorithms for change detection in a surveillance environment. Previous evaluation in the literature has either been subjective or small scale, in part due to the difficulties and/or the time and effort involved in determining appropriate ground truth. In comparison, our automated approach enables us to carry out a more thorough evaluation, and we test the performance of eight different thresholding algorithms using more than 4000 images with two different texture environments.