  Both accuracy and efficiency are of significant importance to the task of
visual object tracking. In recent years, as the surge of deep learning, Deep
Convolutional NeuralNetwork (DCNN) becomes a very popular choice among the
tracking community. However, due to the high computational complexity,
end-to-end visual object trackers can hardly achieve an acceptable inference
time and therefore can difficult to be utilized in many real-world
applications. In this paper, we revisit a hierarchical deep feature-based
visual tracker and found that both the performance and efficiency of the deep
tracker are limited by the poor feature quality. Therefore, we propose a
feature selection module to select more discriminative features for the
trackers. After removing redundant features, our proposed tracker achieves
significant improvements in both performance and efficiency. Finally,
comparisons with state-of-the-art trackers are provided.
