In the field of computer vision, twin network-based tracking algorithms improve accuracy and speed in comparison with traditional algorithms, but they are still affected by target occlusion, deformation, and environmental changes, which leads to the performance degradation of twin network-based tracking algorithms. In order to gain an in-depth understanding of the single target tracking algorithm based on twin networks, the existing target tracking algorithms based on twin networks are summarized and analyzed, mainly including the introduction of attention mechanism method, hyper-parameter inference method and template update method in twin networks, which reviews target tracking algorithms of these three methods and introduces in detail the research and development status of algorithms based on twin networks at home and abroad in recent years. The representative algorithms of the three aspects are experimentally compared using VOT2016, VOT2017, VOT2018 and OTB-2015 datasets to obtain the performance of multiple twin network-based target tracking algorithms. Finally, the twin network-based target tracking algorithms are summarized and the future development direction is prospected.