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. © Springer-Verlag 2013.
CITATION STYLE
Ma, B., Hu, H., Li, P., & Han, Y. (2013). PCA-Based appearance template learning for contour tracking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8228 LNCS, pp. 493–500). https://doi.org/10.1007/978-3-642-42051-1_61
Mendeley helps you to discover research relevant for your work.