Visual Tracking by Adaptive Continual Meta-Learning

Janghoon Choi, Sungyong Baik, Myungsub Choi, Junseok Kwon, Kyoung Mu Lee
2022 IEEE Access  
We formulate the visual tracking problem as a semi-supervised continual learning problem, where only an initial frame is labeled. In contrast to conventional meta-learning based approaches that regard visual tracking as an instance detection problem with a focus on finding good weights for model initialization, we consider both initialization and online update processes simultaneously under our adaptive continual meta-learning framework. The proposed adaptive meta-learning strategy dynamically
more » ... enerates the hyperparameters needed for fast initialization and online update to achieve more robustness via adaptively regulating the learning process. In addition, our continual meta-learning approach based on knowledge distillation scheme helps the tracker adapt to new examples while retaining its knowledge on previously seen examples. We apply our proposed framework to deep learning-based tracking algorithm to obtain noticeable performance gains and competitive results against recent state-of-the-art tracking algorithms while performing at real-time speeds. INDEX TERMS Continual learning, meta learning, object tracking, visual tracking.
doi:10.1109/access.2022.3143809 fatcat:ghc7qvhtafhohe26wzxblbbn7m