  Online updating of the object model via samples from historical frames is of
great importance for accurate 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. 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. More specifically, we first propose the online data
augmentation for tracking that online augments the historical samples through
object-aware filtering. Then, we propose MixNet which is an offline trained
network for performing online data augmentation within one-step, enhancing the
tracking accuracy while preserving high speeds of the state-of-the-art online
learning methods. The extensive experiments on three different tracking
frameworks, i.e., DiMP, DSiam, and SiamRPN++, and three large-scale and
challenging datasets, \ie, OTB-2015, LaSOT, and VOT, demonstrate the
effectiveness and advantages of the proposed method.
