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Fire fighting access occupation self-adaptive detection method based on monitoring video

An adaptive detection and fire exit technology, applied in the field of computer vision, can solve the problems of unsatisfactory accuracy, robustness, and real-time performance, and achieve the effect of avoiding false alarms, reducing false alarms, and improving accuracy

Active Publication Date: 2020-12-25
NANJING ANYUAN TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Artificial monitoring of fire exits consumes a lot of manpower and cannot meet the requirements of accuracy, robustness and real-time

Method used

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  • Fire fighting access occupation self-adaptive detection method based on monitoring video
  • Fire fighting access occupation self-adaptive detection method based on monitoring video
  • Fire fighting access occupation self-adaptive detection method based on monitoring video

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Embodiment Construction

[0043] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0044] The present invention has designed the self-adaptive detection method of fire-fighting channel occupancy based on monitoring video, such as figure 1 As shown, the steps are as follows:

[0045] Step 1: Obtain the video stream corresponding to the camera in the scene to be detected, intercept a video frame image, draw one or more polygons on the video frame image as the key detection area, and set the key detection area for the video stream All subsequent video frames are valid;

[0046] Step 2: Take the pixel value at a certain position of all frames in a video segment, calculate the average value as a standard value for comparison, and use the frame whose pixel value at this position in the video segment is closest to the standard value as the key frame of the video segment ;

[0047] Step 3: Mix Gaussian background modeling, and...

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PUM

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Abstract

The invention discloses a fire fighting access occupation self-adaptive detection method based on a monitoring video, applies a target detection technology in the field of computer vision, is different from other fire fighting access occupation detection methods, does not need to manually construct features, and effectively reduces false alarm and missing alarm situations of a manual feature extraction method. Through a Gaussian mixture background modeling method, an object moving slowly can be detected easily; personnel or fire-fighting vehicles in suspected occupied objects are eliminated byadopting a target detection algorithm, so that the false alarm condition is effectively avoided; and a continuous multi-frame judgment method is adopted, so that the accuracy of system detection is improved, and misinformation is reduced.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for self-adaptive detection of fire exit occupancy. Background technique [0002] "Fire exits" are "life passages", and residents can rest assured only if the "road of life" is kept unimpeded at all times. Fire rescue is a race against time, and blockage of fire exits frequently becomes the biggest obstacle to fire rescue. Once fire exits are blocked or occupied, it will pose a major hidden danger to the safety of residents' lives and properties. Realizing efficient detection and real-time early warning of fire exit occupancy is a problem that needs to be solved urgently in the current society. Artificial monitoring of fire exits consumes a lot of manpower and cannot meet the requirements of accuracy, robustness and real-time performance. Contents of the invention [0003] In order to solve the technical problems mentioned in the above-mentioned b...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/32G06T7/194
CPCG06T7/194G06T2207/10016G06T2207/30232G06V20/52G06V10/25G06V2201/07
Inventor 王三明王聪明云尧王杰胡小敏刘宝
Owner NANJING ANYUAN TECH CO LTD
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