Segmentation for Robust Tracking in the Presence of Severe Occlusion
Camillo Gentile, M Sznaier, O Camps
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.
IEEE International Conference on Computer Vision and Pattern Recognition
, Sznaier, M.
and Camps, O.
Segmentation for Robust Tracking in the Presence of Severe Occlusion, IEEE International Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=51005
(Accessed December 6, 2023)