Abstract
In this paper we discuss the resilience of moving objects detection algorithm based on spatiotemporal blocks on VQriOtLY types of ditive and multiplicative noise. After a given video is decomposed into the spariotemporal blockr, the algorithm uses dimensiondity reduction technique to obtain a compact vector representation of each block and to suppress the injluence of noise. We evaluate the algorithm performance by comparing ''pound truth (hand-labeled moving objects) to properly de$ned spatial-windows based evaluation statistics. Our results on a PETS repository video show that detection and tracking of moving objects is substantially improved in presence of Gawsim, speckle, multiplicative and Poisson noise.
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