A method, system, medium, device and terminal for unmanned aerial vehicle target tracking

The SiamTCP model with pixel-level correlation and cascaded matching addresses the challenges of occlusions and background interference in drone tracking, providing stable and precise target localization.

CN115619829BActive Publication Date: 2025-07-15ZHEJIANG NORMAL UNIV
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Patent Information

Application Number
CN202211384704.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-11-07
Publication Date
2025-07-15
Estimated Expiration
2042-11-07

AI Technical Summary

Technical Problem

The target tracking algorithm of the existing twin network from the perspective of drones is affected by obstacles, changes in the appearance of tracking objects and complex background interference, resulting in unstable tracking. The target information in the first frame contains background information, affecting the matching effect.

Method used

Using a drone target tracking method based on target-aware cascading pixel matching, a SiamTCP model is built through the improved GoogLeNet network and PyTorch framework, feature decomposition and cascading pixel matching are performed, background information is eliminated, and fine-grained matching is performed.

Benefits of technology

It achieves more accurate target tracking in unmanned airport scenes, can cope with target occlusion and deformation in complex scenarios, improves tracking accuracy and speed, and is suitable for applications such as drone navigation and smart agricultural seeding.

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Abstract

The present invention belongs to the technical field of object tracking in computer vision, and discloses a drone object tracking method, system, medium, device and terminal. The backbone network is pre-trained using the ImageNet dataset; a SiamTCP drone tracking model is created, and the SiamTCP drone tracking model is trained on multiple video datasets. The drone object tracking method based on target-aware cascaded pixel matching provided by the present invention determines the parameters of the neural network model by training on multiple datasets. The trained model can be applied to the object tracking task in the drone scenario. Specifically, the present invention proposes pixel-by-pixel cross-correlation and performs a cascaded operation, replacing the global-to-global matching with a part-to-part matching operation, enhancing the result of feature fusion. At the same time, the target-aware module proposed by the present invention can eliminate unnecessary background information and can cooperate well with the cascaded pixel matching process.
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