Abstract: At present， trackers based on siamese networks mainly regard tracking as a cross-correlation calculation between the target template branch and the branch of the region to be detected， which can achieve a good balance between speed and accuracy. However， in the tracking process， the current frame template is a linear combination of the previous cumulative frame template， which leads to the object occlusion difficult to solve. In order to cope with this thorny problem， we adopt the improved SiamRPN tracker and integrate UpdateNet network to track the single pedestrian target. Firstly， the improved SiamRPN network module is used to generate the linear template， then the UpdateNet network is integrated to generate the updated template and perform multi-stage training. Finally， the optimal parameter model is selected to complete the pedestrian tracking task， according to the evaluation index of the dataset. We make the experiment in the benchmark data sets of the OTB2015 and its subset， the results show that the proposed method has obvious improvement than the original method， accuracy and success rate are increased by 2.1 and 1.6 percentage points respectively， while the real-time tracking frame rate is kept. It is also better than many advanced methods to deal with occlusion， such as DaSiamRPN， SiamDW， etc.

Key words: siamese network, UpdateNet network, update template, object occlusion, single pedestrian target