Adaptive rate compressive sensing for background subtraction

We study the problem of adaptive compressive sensing (CS) of a time-varying signal with slowly changing sparsity and rapidly varying support. We are specifically interested in visual surveillance applications such as background subtraction and tracking. Classical CS theory assumes prior knowledge of signal sparsity in order to determine the number of sensor measurements needed to ensure adequate signal reconstruction. However, when dealing with time-varying signals such as video, prior information regarding the exact sparsity may be difficult to obtain. Assuming a sensor that is able to take an adaptive number of compressive measurements, we present an algorithm based on cross validation that quantitatively evaluates the current measurement rate and adjusts it as needed.