PMID: 15376938Sep 21, 2004Paper

Segmentation for robust tracking in the presence of severe occlusion

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Camillo GentileMario Sznaier

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.

References

Feb 12, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·W Y Ma, B S Manjunath
Jan 1, 1997·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·G D BorshukovA M Tekalp

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Citations

Jul 23, 2014·IEEE Transactions on Neural Networks and Learning Systems·Xiao LiuChun Chen
Sep 13, 2011·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Nan JiangYing Wu
Jul 1, 2017·Sensors·Debojit BiswasAleksandar Stevanovic
Sep 1, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Qing Guo Bin Wu

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