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Unmanned aerial vehicle tracking method based on twin neural network and attention model

An attention model and neural network technology, applied in the field of continuous tracking and visualization of single-target unmanned aerial vehicles, can solve the problems of poor discrimination and robustness of the tracker, achieve good generalization performance, universality, and enhanced representation Ability, the effect of facilitating the training process

Inactive Publication Date: 2020-01-10
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0004] The tracking of any target can be understood as finding the object most similar to the target in subsequent images and realizing the positioning of the frame selection mark, that is, learning a function to learn to compare the similarity between the template image and the search image, if the two images describe the same A target returns a high score; use a deep neural network to simulate the above function, and the twin structure is a typical structure of similarity learning; after the image is extracted and mapped to the feature space, a similarity measurement function is used to calculate the similarity score map ; but for deep features, high-level semantic features are robust to changes in target appearance, making the tracker less discriminative

Method used

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  • Unmanned aerial vehicle tracking method based on twin neural network and attention model
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  • Unmanned aerial vehicle tracking method based on twin neural network and attention model

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Embodiment

[0047]This embodiment provides a single UAV tracking method based on twin neural network and attention model, comprising the following steps:

[0048] Step 1: Construct a twin neural network structure and a modular attention model, and use the attention model to enhance the features obtained by the twin network;

[0049] Using the twin neural network with shared parameters to extract the features of the template image Z and the image to be searched for X, the twin network performs the same transformation φ on the two input images to obtain the corresponding feature space F Z and F X :

[0050] f Z = φ(Z)

[0051] f X = φ(X)

[0052] The above transformation φ is a fully convolutional network, and the structure level is:

[0053] [C(96,11,2)-B-R-M(3,2)]-[C(256,5,1)-B-R-M(3,2)]-[C(384,3,1)-B-R]-[ C(384,3,1)-B-R]-[C(256,3,1)]

[0054] Among them, C represents the convolutional layer, B represents the batch normalization layer, R represents the ReLU activation layer, M rep...

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Abstract

The invention relates to the technical field of image processing, in particular to an unmanned aerial vehicle tracking method based on a twin neural network and an attention mechanism, which is applied to continuously tracking a visual single-target unmanned aerial vehicle. According to the method, weight redistribution of channel attention and space attention is realized by using two attention mechanisms, and the representation capability of the model on an unmanned aerial vehicle target appearance model is enhanced by using an attention model for template branches of a twin network; and search images are preprocessed in a multi-scale zooming mode, response graph calculations are separately carried out, inverse transformation of scale changes of the unmanned aerial vehicle in a picture issimulated in the mode, search factors capable of generating larger response values serve as scale inverse transformation of the unmanned aerial vehicle so as to correct the size of a frame used for marking a target, and the transverse-longitudinal proportion of the frame is not changed. According to the method, the tracking precision of 0.513 is obtained through testing (the average coincidence rate is used as a quantization precision standard), and compared with other leading-edge tracking methods, the method has the advantage that the performance is obviously improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an unmanned aerial vehicle tracking method based on a twin neural network and an attention mechanism, which is applied to continuous tracking and visualization of a single-target unmanned aerial vehicle. Background technique [0002] UAV is the abbreviation of unmanned aircraft, which refers to an unmanned aircraft controlled by radio remote control equipment; UAV is mainly used in the military field for reconnaissance, and in the civilian field is widely used in video shooting, aerial photography mapping, disaster relief, etc. However, due to the current industry supervision and policy implementation of the drone industry, there are still problems; presents a huge security risk. The tracking of drones is an effective monitoring method, which can help ground personnel better grasp the flight information of drones and provide powerful help for ground drone countermeasure...

Claims

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Application Information

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IPC IPC(8): G06T7/20
CPCG06T7/20G06T2207/20081G06T2207/20084
Inventor 张萍刘靖雯罗金卢韶强张灵怡
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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