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Segmentation for Robust Tracking in the Presence of Severe Occlusion

Published

Author(s)

Camillo A. Gentile, M Sznaier, O Camps

Abstract

Tracking an object in a sequence of images can fail due to partial occlusion or clutter. Robustness to occlusion can be increased by tracking the object as a set of parts such that not all of these are occluded at the same time. However, successful implementation of this idea hinges upon finding a suitable set of parts. 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 that exhibits a high degree of correlation with the tracking error.
Citation
IEEE Transactions on Image Processing
Volume
13
Issue
2

Keywords

active contours, robust tracking, segmentation

Citation

Gentile, C. , Sznaier, M. and Camps, O. (2004), Segmentation for Robust Tracking in the Presence of Severe Occlusion, IEEE Transactions on Image Processing, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=151135 (Accessed May 20, 2024)

Issues

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Created February 18, 2004, Updated February 19, 2017