A small target tracking method and system based on image enhancement and attention mechanism

By employing image enhancement and attention mechanisms, the problems of insufficient feature information and low spatial positioning accuracy in small target tracking are solved, thus achieving accurate tracking of small targets.

CN122176274APending Publication Date: 2026-06-09CHANGSHA CHAOCHUANG ELECTRONICS TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CHANGSHA CHAOCHUANG ELECTRONICS TECH
Filing Date
2026-02-11
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing small target tracking methods suffer from technical defects such as insufficient feature information extraction, low spatial positioning accuracy, inflexible anchor box generation strategies, and weak attention mechanisms, which lead to a decline in small target tracking performance.

Method used

By employing image enhancement and attention mechanisms, including adaptive histogram equalization, multi-scale feature fusion, coordinate decoupled attention module, and dynamic anchor box generation algorithm, the visual saliency and localization accuracy of small targets are improved.

Benefits of technology

It effectively solves the problems of insufficient feature information and loss of spatial details for small targets, and achieves accurate, robust and efficient tracking of small targets.

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Abstract

This invention discloses a small target tracking method based on image enhancement and attention mechanisms, comprising: performing adaptive histogram equalization on shallow feature maps to enhance local contrast, and fusing with deep semantic features to construct a multi-scale feature pyramid; generating attention weight maps in the horizontal and vertical directions through a coordinate-decoupled attention module to highlight the salient regions of small targets; introducing a dynamic anchor box generation algorithm based on Gaussian distribution, dynamically sampling and generating anchor boxes based on a Gaussian distribution model constructed from the target position in the previous frame; and using the deep cross-correlation layer of a Siamese network to calculate the similarity response map between the template and the search region to determine the center coordinates and bounding box of the small target. This invention effectively solves the problems of insufficient feature extraction, low spatial positioning accuracy, and inflexible anchor box generation in existing methods, significantly improving the accuracy and robustness of small target tracking.
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