An Improved Background Subtraction Method for Adaptive Rate Compressive Sensing

Abstract

Abstract In Compressive Video Sensing application, the using of Adaptive Rate Compressive Sensing (ARCS) method can predict and adjust the sampling rate for each video frame, reduce the total sampling rate and improve the quality of reconstructed image. In order to use the inter-frame correlation of the video signal efficiently and reduce the sampling rate of the whole signal, an Improved Background Subtraction (IBS) method is proposed in this paper. By alternately using the frame without any foreground objects and the previous frame as the background of the signal, the sparsity of the foreground signal is improved and the total sampling rate is reduced. Experimental results show that, compare with the traditional background subtraction method, the IBS method can significantly reduce the total video sampling rate without apparent degradation of reconstructed image quality under the same ARCS method.