TY - GEN
T1 - Adaptive multiple component metric learning for robust visual tracking
AU - Bozorgtabar, Behzad
AU - Goecke, Roland
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Adaptive metric learning
KW - Boosting
KW - Histogram of oriented gradients
KW - Multiple component learning
UR - http://www.scopus.com/inward/record.url?scp=84893378209&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-42051-1_70
DO - 10.1007/978-3-642-42051-1_70
M3 - Conference contribution
AN - SCOPUS:84893378209
SN - 9783642420504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 566
EP - 575
BT - Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
T2 - 20th International Conference on Neural Information Processing, ICONIP 2013
Y2 - 3 November 2013 through 7 November 2013
ER -