A multi branch Siamese network target tracking algorithm based on dual attention mechanism was proposed in order to solve the problem of inaccurate positioning in the SiamRPN++ single target tracking algorithm when the target was briefly occluded and the appearance drastically changed. SiamRPN++ with lightweight backbone network was adopted as the basic algorithm. The algorithm was combined with lightweight channel and spatial attention mechanism in order to improve the anti-interference ability when dealing with occlusion challenges during the tracking process. A template branch was added from the previous frame, and the appearance changes of the target were dynamically updated. The ability to distinguish between foreground and background was enhanced during the tracking process using triplet loss. Local expansion search was conducted based on the speed of the target’s movement in order to enable timely and accurate tracking of the target even after short-term occlusion. The experimental results showed that the improved algorithm improved the success rate and precision of the OTB100 dataset by 2.4% and 1.6%, respectively, compared to the original algorithm. The average center position error decreased by 28.97 pixels, and the average overlap rate increased by 14.5%.