Adaptive Multiple Component Metric Learning for Robust Visual Tracking

Behzad BOZORGTABAR, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

In this paper, we present a new robust visual tracking approach that incorporates an adaptive metric learning in a multiple components framework. Using a similar overall approach to other state-of-the-art tracking methods, which pose object tracking as a binary classification problem, we firstly employ a new feature selection mechanism based on adaptive metric learning for constructing a discriminative target appearance model and then propose a scheme to update the appearance model in a Multiple Component Learning boosting manner, which automatically learns individual component classifiers and combines these into an overall classifier. Experiments on several challenging benchmark video sequences demonstrate the effectiveness and robustness of our proposed method.
Original languageEnglish
Title of host publicationNeural Information Processing - Lecture Notes of Computer Science
EditorsMinho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil
Place of PublicationGermany
PublisherSpringer
Pages566-575
Number of pages10
Volume8228
ISBN (Print)9783642420504
DOIs
Publication statusPublished - 2013
Event20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013

Conference

Conference20th International Conference on Neural Information Processing (ICONIP 2013)
Abbreviated titleICONIP 2013
CountryKorea, Republic of
CityDaegu
Period3/11/137/11/13

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  • Cite this

    BOZORGTABAR, B., & GOECKE, R. (2013). Adaptive Multiple Component Metric Learning for Robust Visual Tracking. In M. Lee, A. Hirose, Z-G. Hou, & R. M. Kil (Eds.), Neural Information Processing - Lecture Notes of Computer Science (Vol. 8228, pp. 566-575). Springer. https://doi.org/10.1007/978-3-642-42051-1_70