The single target tracking algorithm for siamese networks would encounter complex scenes such as background clutter, the influence of similar objects, and occlusion, which leads to a decrease in the accuracy and success rate of the tracking system. In response, this study proposes a tracking algorithm combining the coordinate attention mechanism and template update, i.e., MobileNet coordinate attention and updating of template SiamRPN (MCUSiamRPN). On the basis of the SiamRPN algorithm, the improved MobileNetV3 is used as the feature extraction network, and the multi-layer feature information is sent to the coordinate attention module to fuse features and enrich semantic information. An adaptive template updating module is designed, which combines the initial template and the template of the current frame to estimate the best template of the next frame for template information updating. The test results on OTB100 and UAV123 data sets show that compared with the benchmark algorithm SiamRPN, the proposed one has precision improved by 5.3% and 3.7% and achieves a success rate increased by 3.7% and 5.2%, respectively, which verifies the effectiveness of the developed algorithm.