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In this work, we propose a robust tracking algorithm based on context-aware correlation filter framework. In order to improve the richness of the feature representation, the proposed hyper-feature which contains linearly weighted mixture of hand-crafted features (such as HOG, color histogram) and hierarchical deep convolutional features (such as VGGNet). The final output response map is optimized by the Gaussian constrained optimization method to control the response map follow the Gaussiandoi:10.1109/access.2020.2986157 fatcat:bvzg55ebnjekdj6y3cfbzz3hya