An efficient change detection algorithm based on a statistical nonparametric camera noise model

In this paper we present a change detection algorithm for grey level sequences based on the background subtraction technique, which achieves a good trade-off between time performance and detection quality. The basic idea consists in separating the background process into a deterministic background process and a stochastic camera noise process. The assumption that statistics of the camera noise for a pixel only depends on its current grey level allows to infer a nonparametric statistical camera noise model once and for all arising from a short bootstrap sequence. Hence, 256 couples of lower and upper deterministic thresholds are extracted, to be used in the background subtraction step. While the deterministic nature of the background model as well as of the thresholds lead to an efficient algorithm, utilising 256 couples of different thresholds results in a very sensitive detection. Experimental results allow to assess both the efficiency and the effectiveness of the method we devised.