Deep Convolutional Correlation Filters for Forward-Backward Visual Tracking

In this paper, we exploit convolutional features extracted from multiple layers of a pre-trained deep convolutional neural network. The outputs of the multiple convolutional layers encode both low-level and high-level information about the targets. The earlier convolutional layers provide accurate positional information while the late convolutional layers are invariant to appearance changes and provide more semantic information. Specifically, each convolutional layer locates a target through correlation filter-based tracking and then traces the target backward. By analyzing the forward and backward tracking results, we evaluate the robustness of the tracker in each layer. The final position is determined by fusing the locations from each layer. A region proposal network (RPN) is employed whenever a backward tracker failure occurs. The new position will be chosen from the proposal candidates generated by the RPN. Extensive experiments have been implemented on several benchmark datasets. Our proposed tracking method achieves favorable results compared to state-of-the-art methods.