Robust Visual Tracking based on Deep Spatial Transformer Features
Ximing Zhang, Mingang Wang, Jinkang Wei, Can Cui · 2018
As the development of Artificial Intelligent, visual object tracking plays a key role in computer vision area with numerous real-world applications. This paper proposes a novel approach with the combination of deep spatial transformer features and discriminative correlation filters-based tracking frameworks for the visual tracking problem. The deep spatial transformer features have several advantages compared to the standard deep features (fully connected layers). Firstly, they are more robust when object is suffering affine transformation because of the specific characteristic. Secondly, they have low dimensionality with deeper layer. Lastly, some structural information is contained during tracking procedure. We perform comprehensive experiments on two benchmark datasets: OTB and VOT2015. Surprisingly, compared with some traditional approach based on hand-crafted features, our results further show the affine robustness. We also maintain the accuracy and tracking speed compared with several discriminative correlation filter-based tracker at the same time. Finally, results comparable to state-of-the-art trackers are obtained on all two benchmark datasets.