Many video surveillance applications rely on efficient motion detection. However, the algorithms are usually costly since they compute a background model at every pixel of the frame. This paper shows that, in the case of a planar scene with a fixed calibrated camera, a set of pixels can be selected to compute the background model while ignoring the other pixels for accurate but less costly motion detection. The calibration is used to first define a volume of interest in the real world and to project the volume of interest onto the image, and to define a spatial adaptive subsampling of this region of interest with a subsampling density that depends on the camera distance. Indeed, farther objects need to be analyzed with more precision than closer objects. Tests on many video sequences have integrated this adaptive subsampling to various motion detection techniques. Many, video surveillance applications rely on efficient motion detection. However, the algorithms are usually costly since they compute a background model at every pixel of the frame. This paper shows that, in the case of a planar scene with a fixed calibrated camera, a set of pixels can be selected to compute the background model while ignoring the other pixels for accurate but less costly motion detection. The calibration is used to first define a volume of interest in the real world and to project the volume of interest onto the image, and to define a spatial adaptive subsampling of this region of interest with a subsampling density that depends on the camera distance. Indeed, farther objects need to be analyzed with more precision than closer objects. Tests on many video sequences have integrated this adaptive subsampling to various motion detection techniques.