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Background estimation and update in cluttered surveillance video via the Radon transform

In this paper we propose a background estimation and update algorithm for cluttered video surveillance sequences in indoor scenarios. Taking inspiration from the sophisticated framework of the Beamlets, the implementation we propose here relies on the integration of the Radon transform in the processing chain, applied on a blockby- block basis. During the acquisition of the real-time video, the Radon transform is applied at each frame in order to extract the meaningful information in terms of edges and texture present in the block under analysis, providing with the goal of extracting a signature for each portion of the image plane. The acquired model is updated at each frame, thus achieving a reliable representation of the most relevant details that persist over time for each processed block. The algorithm is validated in typical surveillance contexts and presented in this paper using two video sequences. The first example is an indoor scene with a considerably static background, while the second video belongs to a more complex scenario which is part of the PETS benchmark sequences.