Learning spatial-temporal consistent correlation filter for visual tracking

Discriminative correlation filters (DCF) have aroused great interests in visual object tracking in recent years due to the accuracy and computation efficiency. However, occlusion is still the main factor that affects performance. In this paper, a spatial-temporal consistent correlation filter utilizes the rich features extracted from a pre-trained convolutional neural network (CNN) is proposed to tackle this problem. We reformulate the conventional loss function and update classifier coefficients adaptively according to object appearance change rather than a constant learning rate. To acquire more accurate target location, this work combines correlation filter respond maps from different CNN layers together lie on their reliability. The experimental results evaluated on extensive challenging benchmark sequences demonstrate the proposed algorithm significantly improves the performance compared to state-of-the-art trackers.