Efficient approximate foreground detection for low-resource devices

A broad range of very powerful foreground detection methods exist because this is an essential step in many computer vision algorithms. However, because of memory and computational constraints, simple static background subtraction is very often the technique that is used in practice on a platform with limited resources such as a smart camera. In this paper we propose to apply more powerful techniques on a reduced scan line version of the captured image to construct an approximation of the actual foreground without over burdening the smart camera. We show that the performance of static background subtraction quickly drops outside of a controlled laboratory environment, and that this is not the case for the proposed method because of its ability to update its background model. Furthermore we provide a comparison with foreground detection on a subsampled version of the captured image. We show that with the proposed foreground approximation higher true positive rates can be achieved.