Region-level motion-based foreground detection with shadow removal using MRFs

Shih Shinh Huang*, Li Chen Fu, Pei Yung Hsiao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


This paper presents a new approach to automatic segmentation of foreground objects with shadow removal from an image sequence by integrating techniques of background subtraction and motion-based foreground segmentation. First, a region-based motion segmentation algorithm is proposed to obtain a set of motion-coherence regions and the correspondence among regions at different time instants. Next, we formulate the foreground detection problem as a graph labeling over a region adjacency graph (RAG) based on Markov random fields (MRFs) statistical framework. A background model representing the background scene is built and then is used to model a likelihood energy. Besides the background model, the temporal and spatial coherence are also maintained by modeling it as a prior energy. Finally, a labeling is obtained by maximizing a posterior energy of the MRFs. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2006 - 7th Asian Conference on Computer Vision, Proceedings
Number of pages10
StatePublished - 2006
Event7th Asian Conference on Computer Vision, ACCV 2006 - Hyderabad, India
Duration: 13 01 200616 01 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3851 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th Asian Conference on Computer Vision, ACCV 2006


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