Explicitly exploiting hierarchical features in visual object tracking

A common drawback of convolutional features based trackers is the incapability of distinguishing tracking targets from distractors, even in the presence of distinct color difference. In this paper, we design a robust hybrid tracker that explicitly combines color features with convolutional features. We design our tracker on the basis of fully convolutional Siamese network (SiamFC) to emphasize the performance promotion by introducing color features instead of using a more advanced network architecture. A novel approach to integrate two score maps from different channels is proposed. Techniques include cropping out the ineffective area and denoising via Gaussian smoothing. Experiments conducted on OTB2015 and VOT2018 benchmarks show the superiority of our hybrid tracker over the original SiamFC.