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Constructing a meta-tracker using Dropout to imitate the behavior of an arbitrary black-box tracker
Kourosh Meshgi1, Shin-Ichi Maeda1, Shigeyuki Oba1
1Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo Ward, Kyoto, 606-8501, Japan.
Summary
This study introduces MIMIC, a novel framework for visual tracking that imitates existing algorithms by fusing diverse features. Incorporating Dropout enhances generalization, improving performance in limited training data scenarios.
Area of Science:
- Computer Vision
- Machine Learning
Background:
- Visual tracking algorithms are crucial for various higher-level tasks.
- Understanding black-box trackers and creating super-trackers requires imitation.
- Existing methods face challenges with limited training data, leading to overfitting.
Purpose of the Study:
- To propose MIMIC, a non-linear feature fusion framework for imitating visual trackers.
- To address overfitting in feature selection and weight tuning subtasks.
- To enhance the generalization capability of visual tracking models.
Main Methods:
- Developed a non-linear feature fusion framework named MIMIC.
- Implemented two subtasks: feature selection and feature weight tuning.
- Integrated the Dropout algorithm during training to mitigate overfitting.
Main Results:
- The MIMIC framework effectively imitates popular visual trackers.
- Dropout integration significantly improved the generalization of the trained MIMIC tracker.
- Experimental results validated the framework's effectiveness.
Conclusions:
- The proposed MIMIC framework offers an effective approach for visual tracking imitation.
- Dropout is a valuable technique for enhancing tracker generalization with limited data.
- MIMIC shows promise for broader applications in visual tracking tasks.
