A fuzzy inference approach to template-based visual tracking |
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Authors: | Juan-Pablo Ramirez-Paredes Raul E Sanchez-Yanez Victor Ayala-Ramirez |
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Affiliation: | (1) National Key Lab for Novel Software Technology, Nanjing University, China;(2) HP Labs, Multimedia Interaction and Understanding Lab, Palo Alto, USA;(3) Jiangyin Institute of Information Technology of Nanjing University, China;(4) University of California, Santa Cruz, USA; |
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Abstract: | The tracking of visual features using appearance models is a well studied but still open area of computer vision. In the absence
of knowledge about the structural constraints of the tracked object, the validity of the model can be compromised if only
appearance information is used. We propose a fuzzy inference scheme that can be used to selectively update a given template-based
model in tracking tasks. This allows us to track moving objects under translation, rotation, and scale changes with minimal
feature drift. Moreover, no rigidity constraint needs to be enforced on the moving target. Some experiments have been performed
using several targets, and the results are very close to the ground truth paths. The computational cost of our approach is
low enough to allow its application in real-time tracking using modest hardware requirements. |
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