Robust adaptive learning with Siamese network architecture for visual tracking

Correlation filters and deep learning methods are the two main directions in the research field of visual object tracking. However, these trackers do not balance accuracy and speed very well at the same time. The application of the Siamese networks brings great improvement in accuracy and speed, and an increasing number of researchers are paying attention to this aspect. Therefore, based on the advantages of the Siamese networks model, we conduct feasibility research and improvement of current visual tracking algorithms to improve the tracking performance. In this paper, we propose a robust adaptive learning visual tracking algorithm. HOG features, CN features and deep convolution features are extracted from the template frame and search region frame, respectively, and we analyze the merits of each feature and perform feature adaptive fusion to improve the validity of feature representation. Then, we update the two branch models with two learning change factors and realize a more similar match to locate the target. Besides, we propose a model update strategy that employs the average peak-to-correlation energy (APCE) to determinate whether to update the learning change factors to improve the accuracy of tracking model and reduce the tracking drift in the case of tracking failure, deformation or background blur, etc. Extensive experiments on the benchmark datasets (OTB-50, OTB-100, VOT2016) demonstrate that the proposed visual tracking algorithm is superior to several state-of-the-art methods in terms of accuracy and robustness.