Adaptive Foreground and Shadow Detection inImage Sequences

TL;DR

This paper introduces an adaptive foreground segmentation method that effectively differentiates moving objects from cast shadows in monocular image sequences using Bayesian networks and Markov random fields.

Contribution

It proposes a novel adaptive model integrating background, edge, and shadow information with Bayesian belief networks and Markov random fields for improved segmentation.

Findings

Successfully distinguishes moving objects from shadows.

Adaptive models improve segmentation accuracy.

Utilizes Bayesian and Markov random field techniques.

Abstract

This paper presents a novel method of foreground segmentation that distinguishes moving objects from their moving cast shadows in monocular image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian belief network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. The notion of Markov random field is used to encourage the spatial connectivity of the segmented regions. The solution is obtained by maximizing the posterior possibility density of the segmentation field.