Efficient object tracking using hierarchical convolutional features model and correlation filters

Visual object tracking is a very important task in computer vision. This paper develops a method based on the convolutional neural network (CNN) and correlation filters for visual object tracking. To implement a superior tracking method, we develop a multiple correlation tracker. This paper presents an effective method to track an object based on a combination of feature hierarchies of CNNs. We combine several feature hierarchies and compute the more discriminative map to track the object. Firstly, the correlation filters framework is selected to build the new tracker. Secondly, three feature maps from the CNN, which are inserted into the correlation filters framework, are adopted to evaluate the object location independently. Finally, a novel method of feature hierarchies integration based on Kullback-Leibler (KL) divergence is adopted. Experiments on the different sequences are carried out, and the outputs reveal that the proposed tracker has better results than those of the state-of-the-art methods, and it has the ability to handle various challenges.

Automated summary

This paper proposes a visual object tracking method based on combined feature hierarchies of CNNs and correlation filters, which outperforms existing methods in handling various challenges.