A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts

TL;DR

This paper introduces a low-complexity, block-based sequential algorithm for estimating static backgrounds in cluttered surveillance videos, improving accuracy over existing methods with minimal computational resources.

Contribution

It presents a novel Markov Random Field framework for background estimation that is efficient and effective in cluttered scenes, outperforming median filtering and other recent techniques.

Findings

Better background estimation than median filtering

Improved foreground segmentation when combined with segmentation algorithms

Effective in cluttered, real-life surveillance videos

Abstract

For the purposes of foreground estimation, the true background model is unavailable in many practical circumstances and needs to be estimated from cluttered image sequences. We propose a sequential technique for static background estimation in such conditions, with low computational and memory requirements. 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 background estimation is carried out in a Markov Random Field framework, where the optimal labelling solution is computed using iterated conditional modes. The clique potentials are computed based on the combined frequency response 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 that the proposed method obtains considerably better background estimates (both qualitatively and quantitatively) than median filtering and the recently proposed "intervals of stable intensity" method. Further experiments on the Wallflower dataset suggest that the combination of the proposed method with a foreground segmentation algorithm results in improved foreground segmentation.