Target tracking method and system based on multi-feature map fusion and storage medium

Abstract

The invention requests to protect a target tracking method and system based on multi-feature map fusion and a storage medium. The method comprises the steps: S1, enabling a to-be-tracked target regionimage marked with a position in an initial frame and a current frame search region image to be transmitted to a same feature extraction network to respectively obtain three output feature maps; S2, carrying out convolution on the three output feature maps; S3, respectively sending the fused feature map into a classification branch and a regression branch, obtaining a target position response mapin the classification branch, and obtaining scale transformation and translation of the current position to the target position in the regression branch; and S4, predicting the position of the targetto be tracked in the current frame in combination with the maximum response area of the classification branch and the target movement amount of the regression branch. According to the invention, the robustness and accuracy of the tracking method under the complex conditions of large target scale change and the like are improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a target tracking method and system based on multi-feature fusion of a convolutional neural network. Background technique [0002] The target tracking method belongs to the field of computer vision, and has a wide range of applications in military security entertainment and many other aspects. Target tracking methods are mainly divided into two categories: tracking methods based on traditional methods and tracking methods based on convolutional neural networks. [0003] Among the tracking methods based on traditional methods, correlation filtering is the most representative. The core idea is to use the circular matrix of the area around the target to make positive and negative sample sets during training, and use ridge regression to train a target detector, and use the detector to identify the position of the target during tracking. Because it is offline training, the ...

AI Technical Summary

Problems solved by technology

[0005] Although there are many tracking methods, there are still many challenges in the actual scene, such as illumination changes, background similarity interference, occlusion, target deformation problems, etc.

Although the previous tracking method based on convolutional neural network has greatly improved the real-time performance, there are still some problems with the deformation of the target and the accuracy of prediction.

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