How do deep convolutional features affect tracking performance: an experimental study

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Visual tracking is an import topic in computer vision with many practical applications. Recently, deep learning methods have been introduced into the tracking community to improve tracking performance. How deep features affect tracking performance, however, has not been studied thoroughly. In this paper, we carry out an experimental study to deeply investigate the impact of convolutional features on tracking performance. We adopt the most influential and representative Convolutional Neural Network (CNN) models that are widely used in computer vision to equip our baseline tracking framework. Firstly, we carry out experiments on each CNN to reveal the relationship between tracking performance and different CNN layers. Secondly, we have a vertical comparison of tracking performance between different CNN models. In addition, we explore the effect of ensemble strategies of CNN features on tracking performance. Our work has got several valuable findings on the relationship between tracking performance and convolutional features. Based on our findings, we have derived a few useful guidelines for designing trackers with better performance. We have also developed a simple baseline tracker with the guidelines and it outperforms several state-of-the-art trackers very easily on challenging benchmark video sequences.