Abstract
A novel method is proposed in this paper to model changes of object appearance for object contour tracking. Principal component analysis is utilized to learn eigenvectors from a set of the object appearance in our work, and then the current object appearance can be reconstructed by a linear combination of the eigenvectors. To extract the object contour, we perform covariance matching under the variational level set framework. The proposed method is tested on several sequences under large variations, and demonstrates that it outperforms current methods without updating the appearance template.
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Ma, B., Hu, H., Li, P., Han, Y. (2013). PCA-Based Appearance Template Learning for Contour Tracking. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_61
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DOI: https://doi.org/10.1007/978-3-642-42051-1_61
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