Abstract：Visual tracking is an important research direction of computer vision，single target tracking mainly divided into deep learning，discriminative correlation filters and traditional methods.Benefiting from the efficiency of frequency domain calculations，this paper chooses to select discriminative correlation filters（DCF）as the main point.Firstly，this paper introduces the principle of DCF for visual tracking，and then expands horizontally around the basic framework，to solve the boundary effect becomes a watershed of DCF.With the development of convolutional neural networks，there exist two directions of DCF，feature extraction based on pre-trained model and deep learning combined with the DCF framework.Finally，we summarize an evolution chart of DCF.Compared with the direction of feature interpolation and confidence map fusion，to construct reasonable constraints in the model for model innovation becomes an important direction.The experiment presents the comparison results and ranking of trackers under the OTB-2015 and VOT-2018 datasets，and makes a short analysis.
2020, Vol. 41 
