In order to improve the real-time performance of intelligent surveillance cameras in single target tracking while keeping high tracking accuracy, a tracking method based on the triplets siamese neural network is proposed.Firstly, the backbone of the siamese network is reconstructed based on DenseNet to make full use of feature maps of different levels with less parameters and computation amount.Secondly, the mask branch is added to the siamese network.Then, the similarity scores between the features of target template image and those of the search image are reordered, and the mask is directly generated and refined according to the candidate response window corresponding to the maximum score.Finally, the loss function of the algorithm is defined.The proposed algorithm is evaluated on OTB50/100 and VOT2018 benchmark data set.Experimental results show that, compared with the original SiamMask algorithm, the proposed method is more lightweight while its accuracy is improved, the average frame speed is increased by two times, and the real-time performance is better.