In order to solve the problem that the model drifts due to the motion blur and low resolution when the tracked target moves fast and violently, which leads to the poor tracking effect of the tracker or even the tracking failure, this paper improves the fully convolutional siamese networks for object tracking (SiamFC), and proposes a target tracking algorithm based on conditional generative adversarial siamese networks for object tracking (CGANSiamFC). Firstly, the conditional generative adversarial network module is embedded on the basis of the SiamFC framework to deblur the input low-resolution blurred video frames. Then, the fully convolutional siamese networks perform feature extraction on the reconstructed video frame, which improve the model's characterization ability. Finally, this paper uses separate training and online combination methods to train and test the improved tracking algorithm, and uses the visual tracking benchmark data set OTB100 to evaluate the performance of the improved tracking algorithm. At the same time, in order to fully verify the effectiveness of the proposed algorithm, this paper uses the traditional Lucy-Richardson deblurring algorithm to improve the SiamFC, which is compared with the CGANSiamFC. The experimental results show that the comprehensive accuracy and success rate indicators of the CGANSiamFC proposed are 9% and 8% higher than that of the original SiamFC, respectively, and it has good tracking effects on motion blur and low-resolution moving targets.