In recent years

the correlation filter based algorithm combined with deep features has received extensive attention.The period assumption of the training samples improves the computational efficiency

but also introduces the boundary effect

which limits the further improvement of the tracking performance.By exploring the deep feature representation ability

a new tracking framework is proposed.Since the deep features have good semantic information

the fifth layer convolution feature of VGG network is used to extract the spatially reliable region of the target

and the region information is introduced into the objective function to establish a spatial constraint model.Then iteratively solved by ADMM algorithm.In order to further improve the long-time tracking ability

a simple and effective method of occlusion detection is proposed.Experimental results show that the proposed algorithm outperforms most advanced algorithms in tracking accuracy and success rate.