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Online updating of the object model via samples from historical frames is ofgreat importance for accurate visual object tracking. Recent works mainly focuson constructing effective and efficient updating methods while neglecting thetraining samples for learning discriminative object models, which is also a keypart of a learning problem. In this paper, we propose the DeepMix that takeshistorical samples' embeddings as input and generates augmented embeddingsonline, enhancing the state-of-the-art online learning methods for visualobject tracking. More specifically, we first propose the online dataaugmentation for tracking that online augments the historical samples throughobject-aware filtering. Then, we propose MixNet which is an offline trainednetwork for performing online data augmentation within one-step, enhancing thetracking accuracy while preserving high speeds of the state-of-the-art onlinelearning methods. The extensive experiments on three different trackingframeworks, i.e., DiMP, DSiam, and SiamRPN++, and three large-scale andchallenging datasets, \ie, OTB-2015, LaSOT, and VOT, demonstrate theeffectiveness and advantages of the proposed method.

DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking