An unmanned aerial vehicle visual target tracking method based on scale adaptive kernel correlation filtering

A kernel-related filtering and scale-adaptive technology, applied in computer parts, image data processing, instruments, etc., can solve problems such as long running time, inability to meet UAV tracking requirements, and complex UAV target tracking algorithms

Active Publication Date: 2019-05-28
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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AI Technical Summary

Problems solved by technology

[0005] In order to solve the deficiencies in the prior art, the present invention provides a UAV visual target tracking method based on scale-adaptive kernel correlation fi

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  • An unmanned aerial vehicle visual target tracking method based on scale adaptive kernel correlation filtering
  • An unmanned aerial vehicle visual target tracking method based on scale adaptive kernel correlation filtering
  • An unmanned aerial vehicle visual target tracking method based on scale adaptive kernel correlation filtering

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Embodiment Construction

[0063] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0064] Such as figure 1 As shown, a UAV visual target tracking method based on scale-adaptive kernel correlation filtering, including steps:

[0065] The first step is to select the tracking target to obtain the first frame of data, and use the first frame of data to calculate the initial probability density of the target color and target gradient, and at the same time use the first frame of data to train the classifier and detect the center position of the target using the kernel correlation filter algorithm ;

[0066] The method of calculating the color probability density of the tracking target is as follows:

[0067] Divide the color information of the target area into 16*16*16 levels in ...

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Abstract

The invention discloses an unmanned aerial vehicle visual target tracking method based on scale self-adaptive kernel correlation filtering, which comprises the following steps of selecting a trackingtarget, calculating to obtain the color and gradient initial probability density of a first frame of the tracking target, and training a classifier and detecting the central position of the target byusing the kernel correlation filtering algorithm for the first frame of data; establishing a one-dimensional kernel correlation filter from the second frame to detect the change of the target scale, and calculating kernel correlation filtering by using a convolution theorem; constructing a similarity function by utilizing the current target feature and the initial feature, if the similarity is smaller than a set threshold value, considering that the target identification is inaccurate or the target is lost, entering global search, otherwise, representing that the target is identified and tracked, and obtaining target position information; and sending the position information of the tracking target to an unmanned aerial vehicle flight control system in real time to control the position of the unmanned aerial vehicle. According to the method, the problem of fixed tracking scale of a kernel correlation filtering algorithm is optimized, and the tracking precision of target characteristicsis effectively improved.

Description

technical field [0001] The invention relates to the fields of UAV target tracking and computer vision, in particular to a UAV visual target tracking method based on scale adaptive kernel correlation filtering. Background technique [0002] In recent years, with the rapid development of UAV technology and computer vision technology, vision-based UAV aerial photography has been widely used, especially in the fields of investigation and surveillance, disaster search and rescue, and aerial photography. The multi-axis drone is an unmanned aerial vehicle with simple structure, vertical take-off and landing, and fixed-point hovering. It is composed of forward and reverse pairs of propellers symmetrically distributed on the body. The control of six degrees of freedom is generally driven by lithium batteries. [0003] However, most multi-axis UAVs currently on the market have a low degree of intelligence and automation, and cannot liberate the hands of the operator, which limits the...

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Application Information

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IPC IPC(8): G06T7/246G06K9/00G05D1/10
CPCY02T10/40
Inventor 郭剑东刘青文王康
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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