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Fast splicing method for UAV video

A fast splicing and machine video technology, applied in the field of computer vision, can solve problems such as accelerated nonlinear scale space and weak descriptor robustness, and achieve the goal of simplifying iteration steps, realizing real-time performance, and enhancing robustness and real-time performance Effect

Active Publication Date: 2022-03-04
ARMY ENG UNIV OF PLA
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AI Technical Summary

Problems solved by technology

The algorithm uses the Fast Explicit Diffusion (FED) mathematical framework to dynamically accelerate the calculation of the nonlinear scale space, and proposes a binary descriptor Modified-Local Difference Binary (M-LDB), which greatly improves the speed of the algorithm, but the descriptor The robustness in all aspects is weaker than that of KAZE descriptor

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  • Fast splicing method for UAV video
  • Fast splicing method for UAV video
  • Fast splicing method for UAV video

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Embodiment Construction

[0053] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0054] This implementation mode explains the principle of fast splicing of UAV video, calculates according to the ideas of image preprocessing, feature detection, feature description, feature matching, compaction, and image splicing, and focuses on optimizing and improving the UAV video splicing process . The specific steps are as follows:

[0055] Step 1: According to the task requirements of the UAV video, set the a-th frame as the start frame, and the b-th frame as the end frame, extract a frame every m frames, and get all the frames to be processed; among them, the number of frames is [b-a / m]; among them, [] is rounding symbol.

[0056] Step 2: Use the downsampling algorithm to downsample all the frames to be proce...

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Abstract

The invention discloses a video splicing method of an unmanned aerial vehicle, and relates to the technical field of computer vision. The focus of this algorithm is to enhance the simplicity and real-time performance of UAV video stitching. The experimental results show that FARISFD and ROGFD are more robust and real-time than traditional feature detectors and descriptors. RSCFDI is more robust and real-time than traditional algorithms. It simplifies the iterative steps of building the model and improves the efficiency of obtaining the correct model. The fast video stitching method of UAV can realize real-time video stitching, which is an important improvement to the existing technology.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for quickly splicing videos of unmanned aerial vehicles. Background technique [0002] In recent years, UAV camera technology has developed rapidly in various fields, but the reconnaissance video has a small size, the body shakes, noise and large light changes, and the use of POS data to rectify inaccurate and ground control points are difficult to obtain and other factors , seriously affecting the video registration effect and speed. Therefore, it is of great practical significance to study a fast registration method for UAV reconnaissance video. [0003] With the rapid development of computer vision technology, aerial image registration has entered the stage of automatic image matching. In recent years, feature matching has been widely used and studied. [0004] In terms of feature matching, scholars have done a lot of work: Lowe proposed the classic Scale In...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40
CPCG06T3/4038
Inventor 胡永江张岩李爱华李建增张玉华褚丽娜李文广赵月飞刘新海
Owner ARMY ENG UNIV OF PLA