Improved Foreground Detection via Block-based Classifier Cascade with Probabilistic Decision Integration

TL;DR

This paper introduces a block-based foreground detection method that improves accuracy and robustness by integrating contextual information through a classifier cascade and probabilistic decision fusion, enhancing tracking performance.

Contribution

It presents a novel block-based approach with a classifier cascade and probabilistic decision integration that outperforms pixel-based methods without needing post-processing.

Findings

Better qualitative and quantitative results on Wallflower and I2R datasets.

Significant improvements in tracking accuracy on CAVIAR dataset.

No ad-hoc post-processing required for foreground masks.

Abstract

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analysed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc post-processing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.