Segmentation for robust tracking in the presence of severe occlusion.
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.
Gentile
Camillo
C
Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USA. camillo@gandalf.ee.psu.edu
Camps
Octavia
O
Sznaier
Mario
M
D000465:Algorithms; D000818:Animals; D016477:Artifacts; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*