CNN tracking based on data augmentation

Correlation filter based tracking methods have aroused increasing attention due to the appealing performance on tracking benchmark datasets. For each frame, a filter is trained to separate the object from its background. Considering that the object always undergoes challenging situations, the trained filter should consider both external and internal distractions. In this paper, we propose a data augmentation based robust visual tracking algorithm to better generalize the training data. Specifically, data augmentation technique is utilized to generate training samples to improve the robustness of the training filter. Then hierarchical convolutional neural network (CNN) features are utilized to encode the target and the augmented sample. Different from previous work, we exploit to use a hash matrix to reduce the dimension of the CNN features. Next, the correlation filter tracking method is employed. The tracking results of multiple hash features are combined to locate the target. Extensive experiments on five large scale datasets show that the proposed method achieves comparable results to state-of-the-art trackers.