Deep Meta Learning for Real-Time Visual Tracking based on Target-Specific Feature Space

In this paper, we propose a novel on-line visual tracking framework based on Siamese matching network and metalearner network which runs at real-time speed. Conventional deep convolutional feature based discriminative visual tracking algorithms require continuous re-training of classifiers or correlation filters for solving complex optimization tasks to adapt to the new appearance of a target object. To remove this process, our proposed algorithm incorporates and utilizes a meta-learner network to provide the matching network with new appearance information of the target object by adding the target-aware feature space. The parameters for the target-specific feature space are provided instantly from a single forward-pass of the meta-learner network. By eliminating the necessity of continuously solving the complex optimization tasks in the course of tracking, experimental results demonstrate that our algorithm performs at a real-time speed of 62 fps while maintaining a competitive performance among other stateof-the-art tracking algorithms.