Metrics for Objective Evaluation of Background Subtraction Algorithms

Although a large number of background subtraction (BS) algorithms have been proposed, relevant objective metrics for evaluating these algorithms are still lacking. In this paper, empirical discrepancy metrics, which quantify the spatial accuracy and temporal stability of estimated masks by taking into account the potential inaccuracy of reference masks, the location of the pixel errors relative to the border of reference masks as well as the type of errors, are presented for evaluating the performance of BS algorithms. To validate the proposed metrics, they are applied to tune the optimal parameters of LBP-based background subtraction algorithm, and the experimental results confirm the efficiency of them.