An efficient background estimation algorithm for embedded smart cameras

Abstract

Segmentation of foreground objects of interest from an image sequence is an important task in most smart cameras. Background subtraction is a popular and efficient technique used for segmentation. The method assumes that a background model of the scene under analysis is known. However, in many practical circumstances it is unavailable and needs to be estimated from cluttered image sequences. With embedded systems as the target platform, in this paper we propose a sequential technique for background estimation in such conditions, with low computational and memory requirements. The first stage is somewhat similar to that of the recently proposed agglomerative clustering background estimation method, where image sequences are analysed on a block by block basis. For each block location a representative set is maintained which contains distinct blocks obtained along its temporal line. The novelties lie in iteratively filling in background areas by selecting the most appropriate candidate blocks according to the combined frequency responses of extended versions of the candidate block and its neighbourhood. It is assumed that the most appropriate block results in the smoothest response, indirectly enforcing the spatial continuity of structures within a scene. Experiments on real-life surveillance videos demonstrate the advantages of the proposed method.