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Forest fire smoke video target detection method based on characteristic roots and hydromechanics

A fluid mechanics and target detection technology, applied in the field of forest fire detection based on video, can solve the problems of ignoring the natural environment of the smoke generation point and not bringing predictable result judgment

Active Publication Date: 2018-05-08
BEIJING FORESTRY UNIVERSITY
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Problems solved by technology

More importantly, it ignores the attention to the natural environment around the smoke generation point, such as wind speed, so it cannot bring predictive result determination

Method used

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  • Forest fire smoke video target detection method based on characteristic roots and hydromechanics
  • Forest fire smoke video target detection method based on characteristic roots and hydromechanics
  • Forest fire smoke video target detection method based on characteristic roots and hydromechanics

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Embodiment Construction

[0065] 1. A forest fire smoke video target detection method based on eigenroot and hydrodynamics, comprising the following steps:

[0066] Step 1: Start smoke detection from the i-th frame of the set frame number of the video, and take the i-th frame video image I i Perform color-grayscale conversion to obtain a grayscale image G i , set the image stack calculation area with a length of 6, and calculate the Ith i+j , j=1,2,3,4, the grayscale image G of the frame image i+j , the G i+k , k=0,1,...,5, a total of 6 grayscale images are stored in the image stack;

[0067] Step 2: Perform an inter-frame difference algorithm on the 6 grayscale images stored in the image stack to obtain 5 difference images D i+j , the difference adopts the forward difference method, where D i+2 As the core judgment image of the current frame, the inter-frame difference formula is as follows:

[0068]

[0069] where G i (x,y) is the grayscale image of the i-th frame image, G i+1 (x, y) is th...

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Abstract

The invention discloses a forest fire smoke video target detection method based on characteristic roots and hydromechanics. The method comprises: firstly, extracting a dynamic region from a video by extracting continuous frames of image from the video; secondly, using a morphological algorithm to calculate the connected region skeleton image of the dynamic region; then extracting suspicious smokeroot characteristic candidate points in the endpoints of continuous frames of skeleton image; subjecting the smoke root characteristic candidate points to determination based on a Navier-Stokes equation to obtain the predicted smoke image of the smoke root characteristic; and finally fusing and determining the predicted image and a current actual image to obtain a smoke area. Because the algorithmhas certain predictability and can continuously affect the decision information of continuous frames, a determined result is stable and accidental calculation errors caused by an image processing stage can be avoided.

Description

technical field [0001] The invention belongs to the fields of forest fire prevention and video target detection, and in particular relates to a video-based forest fire detection method. Background technique [0002] The mainstream methods of smoke detection technology based on video images can be roughly divided into three categories: color intensity-based, dynamic detection-based, and texture-based detection. Although new types of detection methods such as feature fusion, multi-feature extraction, and optical flow have emerged during the development of detection methods, the root cause is still the update and fusion of the three major features of color, dynamics, and texture. This characteristic determines the rigid requirements of the method for image quality, and requires specific threshold debugging and detection for different scenarios. Although the emerging feature classification method based on deep learning network is less dependent on the threshold, the large amoun...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/46G06F18/253
Inventor 程朋乐高宇周茗岩
Owner BEIJING FORESTRY UNIVERSITY
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