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We propose a framework called ReFInE to directly obtain integral imageestimates from a very small number of spatially multiplexed measurements of thescene without iterative reconstruction of any auxiliary image, and demonstratetheir practical utility in visual object tracking. Specifically, we designmeasurement matrices which are tailored to facilitate extremely fast estimationof the integral image, by using a single-shot linear operation on the measuredvector. Leveraging a prior model for the images, we formulate a nuclear normminimization problem with second order conic constraints to jointly obtain themeasurement matrix and the linear operator. Through qualitative andquantitative experiments, we show that high quality integral image estimatescan be obtained using our framework at very low measurement rates. Further, ona standard dataset of 50 videos, we present object tracking results which arecomparable to the state-of-the-art methods, even at an extremely lowmeasurement rate of 1%.

Fast Integral Image Estimation at 1% measurement rate