Method for realizing target identification by Faster-Vibe and classification algorithm

A target recognition and classification algorithm technology, applied in neural learning methods, character and pattern recognition, computing, etc., can solve problems such as ghosting, holes in images, and large amount of calculations in frame difference methods, so as to reduce interference, improve management, and The effect of increased computing speed

Pending Publication Date: 2022-04-12
CHINESE FLIGHT TEST ESTAB
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Problems solved by technology

[0005] 1) The frame difference method has a large amount of calculation, and holes will appear in the image;
[0006] 2) Gaussian background modeling consumes a lot of system resources. When the light changes suddenly, it is easy to cause a large area of ​​false detection. When a long-term stationary object turns into m...

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  • Method for realizing target identification by Faster-Vibe and classification algorithm
  • Method for realizing target identification by Faster-Vibe and classification algorithm
  • Method for realizing target identification by Faster-Vibe and classification algorithm

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Embodiment Construction

[0045] The present invention is elaborated in conjunction with accompanying drawing:

[0046] The present invention provides a kind of Faster-Vibe and classification algorithm to realize target recognition method, such as figure 1 shown, including:

[0047] (1) By performing an uneven downsampling operation on the input image, the pixels of the image are reduced while ensuring that the target features are not lost, reducing the amount of calculation and improving the detection speed. Assuming that the original image is divided into n regions rect 1 , rect 2 ,...,rect n , the corresponding area down_rect after downsampling 1 ,down_rect2,...,down_rect n , the scale coefficient of the corresponding area is s 1 ,s 2 ,...,s n ;

[0048] (2) Initialize the non-uniform downsampled image: Assume that the pixel values ​​of each pixel and its neighbor pixels have similar distributions in the spatial domain. When the first frame of image is input, that is, when t=0, the backgro...

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Abstract

The invention belongs to the field of target detection and recognition, and particularly relates to a method for achieving target recognition through Faster-Vibe and a classification algorithm. The method comprises the following steps: partitioning an input image according to pixel gray scale difference; wherein the large-gray-difference area is large in size, and the small-gray-difference area is small in size; removing areas with small gray difference; the input image may include a person, a vehicle, a small aircraft and a large aircraft; non-uniform down-sampling operation is carried out on the non-rejected area to obtain a corresponding down-sampling subarea; identifying whether the pixel of each down-sampling partition is a foreground pixel or not based on a Vibe algorithm; taking continuous foreground pixels on each down-sampling partition as a pixel set of a suspected target; processing a plurality of holes of the target according to the pixel set of the suspected target, and combining the suspected target into the same target; and inputting the pixel set of the same target into a pre-trained lightweight classification network, and determining which type of the target is a person, a vehicle, a small aircraft and a large aircraft.

Description

technical field [0001] The invention belongs to the field of target detection and recognition, in particular to a method for realizing a target recognition by Faster-Vibe and a classification algorithm. [0002] technical background [0003] In the application of airport video surveillance, real-time monitoring of specific targets in the video is required. Since the scale and angle of the monitored target will change greatly in the video, especially when multiple targets appear, relying on manual monitoring of all moving objects in the video screen will not only fail to monitor every moving object, but will also consume a lot of manpower , time and energy. [0004] Analyze the existing moving target detection algorithm: [0005] 1) The frame difference method has a large amount of calculation, and holes will appear in the image; [0006] 2) Gaussian background modeling consumes a lot of system resources. When the light changes suddenly, it is easy to cause a large area of ...

Claims

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Application Information

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IPC IPC(8): G06V20/52G06V20/40G06V10/28G06V10/26G06V10/764G06V10/82G06K9/62G06T7/207G06N3/04G06N3/08
Inventor 李宏刚梁恩泽杜双朱泉兴姚佳磊
Owner CHINESE FLIGHT TEST ESTAB
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