title: Constructing a meta-tracker using Dropout to imitate the behavior of an arbitrary black-box tracker.
abstract: Imitating the behaviors of an arbitrary visual tracking algorithm enables many higher level tasks such as tracker identification and efficient tracker-fusion. It is also useful for discovering the features essential in a black-box tracker or learning from several trackers to form a super-tracker. In this study, we propose a non-linear feature fusion framework, "MIMIC" that imitates many popular trackers by mixing a pool of heterogeneous features. The MIMIC framework consists of two subtasks, feature selection and feature weight tuning. These subtasks, however, tended to suffer from an overfitting problem when the number of videos available for training is limited. To address this issue, we incorporated Dropout algorithm into the training, which grants the trained MIMIC tracker a high degree of generalization. Extensive experiments testified the effectiveness of the proposed framework so that its applications would be promoted into different related tasks in visual tracking.
authors.affiliation: Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo Ward, Kyoto, 606-8501, Japan. Electronic address: meshgi-k@sys.i.kyoto-u.ac.jp.
authors.forename: Kourosh
authors.initials: K
authors.lastname: Meshgi
authors.affiliation: Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo Ward, Kyoto, 606-8501, Japan.
authors.forename: Shin-Ichi
authors.initials: SI
authors.lastname: Maeda
authors.affiliation: Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo Ward, Kyoto, 606-8501, Japan.
authors.forename: Shigeyuki
authors.initials: S
authors.lastname: Oba
authors.affiliation: Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo Ward, Kyoto, 606-8501, Japan.
authors.forename: Shin
authors.initials: S
authors.lastname: Ishii
mesh_terms: D000465:Algorithms; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods
keywords: Dropout; Features selection; Generalization; Tracking