To improve the tracking performance in complex scenarios,an object tracking algorithm with feature enhancement and dual-template updating is proposed.Firstly,an improved feature extraction network is proposed,and deep-layer features and shallow-layer features are fused,and then the spatial-channel attention module is utilized to enhance the fused features,so that better capability of feature representation is obtained.Secondly,a dual-template updating strategy is proposed,and the adjacent high-confidence frame of the image is reserved as the backup template,which is utilized to implement weighted fusion with the initial template to obtain new templates for conducting tracking prediction again when the confidence of the tracking response map is low.Finally,the tracking performance is evaluated on the datasets of OTB-100 and VOT-2017,and the experimental results show that the proposed algorithm improves the tracking success rate and accuracy in complex scenarios such as occlusion,illumination change and background clutter.