DCFNet: Discriminant Correlation Filters Network for Visual Tracking

CNN (convolutional neural network)-based real time trackers usually do not carry out online network update in order to maintain rapid tracking speed. This inevitably influences the adaptability to changes in object appearance. Correlation filter-based trackers can update the model parameters online in real time. In this paper, we present an end-to-end lightweight network architecture, namely the discriminant correlation filter network (DCFNet). A differentiable DCF layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter simultaneously. The correlation filter can be efficiently updated online. We introduce a joint scale-position space to the DCFNet, forming a scale DCFNet which carries out the predictions of object scale and position simultaneously. We combine the scale DCFNet with the convolutional-deconvolutional network, learning both the high-level embedding space representations and the low-level fine-grained representations for images. The adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual tracking. The back-propagation is derived in the Fourier frequency domain throughout the entire work, preserving the efficiency of the DCF. Extensive evaluations on the OTB (object tracking benchmark) and VOT (visual object tracking challenge) datasets demonstrate that the proposed trackers have fast speeds, while maintaining tracking accuracy.