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Aiming at alleviate the detrimental effect of similar object interferences and target state changes in SiamRPN tracker, a Channel Positive and Negative Feedback Network (CPFN) is proposed, in which the Gaussian score map is generated by the feature channels selected by a Gaussian kernel, and the map is combined with the classification branches of SiamRPN. In this way, the feature channels are divided into positive feedback channels and interference channels, and these feature channels are effectively utilized. In addition, a channel weight update strategy is proposed to enhance the robustness of the tracker and avoid template pollution caused by inadequate template update. Extensive experiments on tracking benchmarks including VOT2016, VOT2018, VOT2019, OTB100, UAV123, LaSOT and GOT-10k show that the proposed CPFN outperforms the state-of-the-art methods based on small backbone network in terms of accuracy and achieves high-speed tracking. INDEX TERMS Target tracking, siamese network, gaussian kernel, feature combination [13]. With the development of deep learning, deep features have shown excellent effects in image processing and are widely used in image classification, face recognition, object

Target tracking is one of the challenging tasks in computer vision. Usually, the center of target origins from the position with the largest response value, and the key to improving tracking performance is to learn reliable feature maps. This paper analyzes the characteristics of the tracking task, designs a global feature comparison function to extract the context, and proposes a feature supplement module based on the global comparison information for further performance improvement. In addition, we also design a template feature update module to supplement template features based on the search area features of the current frame to dynamically adjust model features, improve model generalization capabilities, and avoid model feature fixation. The proposed feature supplement model based on global feature comparison (FSGFC) is evaluated on five visual tracking benchmarks including OTB100, VOT2016, VOT2018, VOT2019 and UAV123. The experimental results show that the model obtains the state-of-the-art performance with a real-time speed.INDEX TERMS Target tracking, Siamese network, feature extraction, template update.

We introduce a group of virtual sources for generating Lommel–Gauss beams based on beam superposition to analyze nonparaxial propagation. We typically derive the paraxial approximation and integral representations of the nth-order Lommel–Gauss beams. The first three orders of the nonparaxial corrections for the on-axis field of the Lommel–Gauss beams are analytically obtained. The on-axis intensity distribution of the corresponding nonparaxial corrections is also provided.

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