![]() More specifically, we first propose the online data augmentation for tracking that online augments the historical samples through object-aware filtering. ![]() In this paper, we propose the DeepMix that takes historical samples' embeddings as input and generates augmented embeddings online, enhancing the state-of-the-art online learning methods for visual object tracking. Recent works mainly focus on constructing effective and efficient updating methods while neglecting the training samples for learning discriminative object models, which is also a key part of a learning problem. ![]() Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking.
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