Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies

Kelly, Philip, Beardsley, Paul, Cooke, Eddie, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2005) Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies. In: VIE 2005 - The IEE International Conference on Visual Information Engineering, Convergence in Graphics and Vision, 4-6 April 2005, Glasgow, UK.

Abstract
Many computer vision applications apply background suppression techniques for the detection and segmentation of moving objects in a scene. While these algorithms tend to work well in controlled conditions they often fail when applied to unconstrained real-world environments. This paper describes a system that detects and removes erroneously segmented foreground regions that are close to a ground plane. These regions include shadows, changing background objects and other low-lying objects such as leaves and rubbish. The system uses a set-up of two or more cameras and requires no 3D reconstruction or depth analysis of the regions. Therefore, a strong camera calibration of the set-up is not necessary. A geometric constraint called a homography is exploited to determine if foreground points are on or above the ground plane. The system takes advantage of the fact that regions in images off the homography plane will not correspond after a homography transformation. Experimental results using real world scenes from a pedestrian tracking application illustrate the effectiveness of the proposed approach.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:cameras; computer vision; image motion analysis; image segmentation; object detection; road traffic; stereo image processing;
Subjects:Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Research Initiatives and Centres > Adaptive Information Cluster (AIC)
Publisher:Institution of Engineering and Technology
Official URL:http://dx.doi.org/10.1049/cp:20050118
Copyright Information:This paper is a postprint of a paper submitted to and accepted for publication in IEE International Conference on Visual Information Engineering (VIE 2005) and is subject to IET copyright. The copy of record is available at IET Digital Library http://dx.doi.org/10.1049/cp:20050118 .
Funders:Science Foundation Ireland, SFI 03/IN.3/I361
ID Code:283
Deposited On:11 Mar 2008 by DORAS Administrator . Last Modified 08 Nov 2018 11:03
Documents

Full text available as:

[thumbnail of iet_vie_2005.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record