Active Attentional Sampling for Speed-up of Background Subtraction

Abstract

In this paper, we present an active sampling method to speed up conventional pixel-wise background subtraction algorithms. The proposed active sampling strategy is designed to focus on attentional region such as foreground regions. The attentional region is estimated by detection results of previous frame in a recursive probabilistic way. For the estimation of the attentional region, we propose a foreground probability map based on temporal, spatial, and frequency properties of foregrounds. By using this foreground probability map, active attentional sampling scheme is developed to make a minimal sampling mask covering almost foregrounds. The effectiveness of the proposed active sampling method is shown through various experiments. The proposed masking method successfully speeds up pixel-wise background subtraction methods approximately 6.6 times without deteriorating detection performance. Also realtime detection with Full HD video is successfully achieved through various conventional background subtraction algorithms.