Aiming at problem of scene dependent and can not adapt to the change of target size of kernel correlation filter tracking algorithm

propose a variable scale kernel correlation filtering tracking algorithm based on feature selection and analysis. Within the framework of filting tracking of kernel correlation extract color

texture

gradient feature of target

set up sample set. According to the minimum mean square loss function

set sample weight of each feature

differentially select optimal and suboptimal two kinds of features for adaptive fusion. Using Gaussian Pyramid to construct ID scale correlation response filter

to estimate scale variations

compare sidelobe mean value with tracking results fusion to realize difference update of the template of target. Theoretical analysis and experimental results show that the proposed algorithm has higher tracking presision and has certain estimation ability to scale variation of target.