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2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 2
Segmentation for Robust Tracking in the Presence of Severe Occlusion
Kauai, Hawaii
December 08-December 14
ISBN: 0-7695-1272-0
ASCII Text
x 
 
Camillo Gentile, Octavia Camps, Mario Sznaier, "Segmentation for Robust Tracking in the Presence of Severe Occlusion," Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 2, pp. 483, 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 2, 2001.
 
 
BibTex
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@article{ 10.1109/CVPR.2001.991001,
author = {Camillo Gentile and Octavia Camps and Mario Sznaier},
title = {Segmentation for Robust Tracking in the Presence of Severe Occlusion},
journal ={Computer Vision and Pattern Recognition, IEEE Computer Society Conference on},
volume = {2},
year = {2001},
issn = {1063-6919},
pages = {483},
doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2001.991001},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
TI - Segmentation for Robust Tracking in the Presence of Severe Occlusion
SN - 1063-6919
SP
EP
A1 - Camillo Gentile,
A1 - Octavia Camps,
A1 - Mario Sznaier,
PY - 2001
KW - null
VL - 2
JA - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
ER -
 
Camillo Gentile, National Institute of Standards and Technology
Octavia Camps, The Pennsylvania State University
Mario Sznaier, The Pennsylvania State University
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 out-performs 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:
Camillo Gentile, Octavia Camps, Mario Sznaier, "Segmentation for Robust Tracking in the Presence of Severe Occlusion," cvpr, vol. 2, pp.483, 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 2, 2001
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