Gentile, C.
, Sznaier, M.
and Camps, O.
(2001),
Segmentation for Robust Tracking in the Presence of Severe Occlusion, IEEE Conference on Computer Vision and Pattern Recognition Technical, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=151101
(Accessed October 2, 2025)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Segmentation for Robust Tracking in the Presence of Severe Occlusion
Published
Author(s)
, M Sznaier, O Camps
Abstract
Tracking an object in a sequence of images can fail due to partial occlusion or clutter. Robustness can be increased by tracking a set of parts, provided that a suitable set can be identified. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function highly correlated with the tracking error.Citation
IEEE Conference on Computer Vision and Pattern Recognition Technical
Pub Type
Journals
Download Paper
Keywords
images, tracking
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact [email protected].
Created December 3, 2001, Updated February 19, 2017