Unmanned aerial vehicle tracking method based on KCF

A UAV and algorithm technology, applied in the field of UAV tracking based on KCF, can solve the problems of being unable to find the target, the target is lost, etc., and achieve the effect of simple principle, improved real-time performance, and real-time performance

Inactive Publication Date: 2020-05-19
HUNAN NOVASKY ELECTRONICS TECH
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  • Unmanned aerial vehicle tracking method based on KCF
  • Unmanned aerial vehicle tracking method based on KCF
  • Unmanned aerial vehicle tracking method based on KCF

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[0040] The present invention will be further described in detail below with reference to the drawings and specific embodiments of the specification.

[0041] Such as image 3 As shown, the KCF-based UAV tracking method of the present invention includes:

[0042] Step S1: input image sequence;

[0043] Step S2: Use a coarse to fine search strategy to extract m image patches with the greatest similarity from the input image sequence;

[0044] Step S3: Extract FHOG, grayscale, and CN features from m image patches, and record them as feature maps; use PCA-entropy weight method to reduce dimensionality of m feature maps;

[0045] Step S4: Find the image patch corresponding to the maximum value according to the KCF algorithm of context-aware correlation filter tracking (KCF algorithm of context-aware correlation filter tracking) for the reduced feature maps corresponding to the m image patches, which is recorded as FindImage;

[0046] Step S5: Perform a fine search on FindImage according to th...

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Abstract

The invention discloses an unmanned aerial vehicle tracking method based on KCF. The method comprises the following steps: S1, inputting an image sequence; S2, m image patches with the maximum similarity are extracted from an input image sequence by utilizing a coarse to fin search strategy; S3, FHOG, gray scale and CN characteristics are extracted from the m image patches respectively, and the extracted FHOG, gray scale and CN characteristics are recorded as a feature map; respectively carrying out dimension reduction on the m feature maps by using a PCA-entropy weight method; S4, finding theimage patch corresponding to the maximum value of the dimension-reduced feature maps corresponding to the m image patches according to a KCF algorithm of background perception, and recording the image patch corresponding to the maximum value as FindImage; and S5, carrying out fine search on the FindImage according to a KCF algorithm, and finding an accurate position of the target. The method hasthe advantages of being simple in principle, easy to implement, good in real-time performance, capable of solving the problem of target rapid movement tracking loss and the like.

Description

technical field [0001] The invention mainly relates to the technical field of unmanned aerial vehicle detection, in particular to a KCF-based unmanned aerial vehicle tracking method. Background technique [0002] UAV is a typical low-slow and small target. Its characteristics include low flying altitude, slow speed, small effective detection area, and not easy to be detected. These characteristics of UAVs mean that traditional air threat detection systems are no longer applicable, and corresponding anti-UAV systems must be developed for these characteristics of UAVs to achieve threat detection of intruding UAVs. [0003] Some practitioners proposed the document "Design and Implementation of Vision-Based UAV Intrusion Detection and Tracking System", which proposed to use Kalman filter to improve the problem of UAV moving too fast, but when the UAV is moving When turning suddenly in the middle, this method will cause the problem of tracking loss. [0004] Another practitione...

Claims

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Application Information

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IPC IPC(8): G06T7/246G06T7/73G06T7/90
CPCG06T2207/10016G06T7/246G06T7/73G06T7/90
Inventor 杨松韩明华韩乃军唐良勇
Owner HUNAN NOVASKY ELECTRONICS TECH
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