Cost-sensitive background subtraction

Foreground and background are treated without distinction at classification stage in most background subtraction algorithms. However, correct classification of foreground is the primary requirement, and thus misclassification costs of the two classes should be different. Based on this fact, we present a new method to introduce cost sensitivity into background subtraction, where a cost matrix is created to represent the costs of misclassification. Some items in the cost matrix are not constants, but functions of foreground occurence at each pixel location. By the use of such non-constant costs, detection rate of foreground is improved while increase of false alarms is prevented at the same time. Experiments demonstrate the effectiveness of the proposed algorithm.