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The invention discloses an AI thermal imaging all-weather intelligent monitoring method

An intelligent monitoring and thermal imaging technology, applied in the field of thermal imaging, can solve the problems of lack of intelligent algorithm deployment and high false alarm probability, etc., and achieve the effect of excellent loss function, lower false positive rate, and improve accuracy

Inactive Publication Date: 2019-06-28
CHENGDU UNIVERSITY OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the cloud thermal imaging system still lacks a sound intelligent algorithm deployment on the back-end cloud server. Especially in the field of security and anti-terrorism applications, the mainstream uses motion detection and heat source tracking algorithms. high probability of false positive

Method used

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  • The invention discloses an AI thermal imaging all-weather intelligent monitoring method
  • The invention discloses an AI thermal imaging all-weather intelligent monitoring method
  • The invention discloses an AI thermal imaging all-weather intelligent monitoring method

Examples

Experimental program
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Effect test

Embodiment 1

[0059] In the AI ​​thermal imaging all-weather intelligent monitoring method provided by a preferred embodiment of the present invention, the motion detection algorithm in S3 is a PBAS motion detection algorithm, and its workflow is as follows:

[0060] S31: Collect the pixels and gradient magnitudes of the previous N frames as the background model, and compare the newly arrived pixels with the background model. The comparison method is as follows:

[0061]

[0062] In the formula, I(xi) is the input, Bk(xi) represents the pixels in the background model, R(xi) represents the pixel threshold, min represents the minimum number of matches, and F(xi)=1 represents the foreground;

[0063] The judgment threshold R(xi) is calculated by the following formula:

[0064]

[0065] In the formula, Dk(xi) is the average value of the N minimum values ​​of the input pixel.

[0066] After the moving target is detected, in order to eliminate noise and light and shadow interference, confi...

Embodiment 2

[0069] In this embodiment, on the basis of Embodiment 1, the target of interest is segmented in S4 using the MASK-RCNN segmentation framework based on deep learning technology in artificial intelligence, and its workflow is: S41: Select the following for video images with significant motion Targets of interest are segmented: pedestrians, cars, trucks, motorcycles, bicycles and boats, and the segmentation threshold is set to 0.6; S42: Use the non-maximum suppression algorithm (NMS) to eliminate redundant segmentation windows and find the best target segmentation position.

Embodiment 3

[0071] In this embodiment, on the basis of Embodiment 2, the single or multi-target tracking workflow in S5 is:

[0072] S51. Determine the number of currently segmented targets of interest, and start the tracking mode as a single target or multiple targets. The reason why the target tracking link is performed is that the target segmentation speed is slow, and it is difficult to ensure the real-time performance of the system. Therefore, save the early warning video and switch to target tracking mode;

[0073] S52. Use the high-performance target tracking algorithm CSR-DCF, which is a tracking algorithm based on correlation filtering, which is improved by using the "spatial confidence" method; its main idea is to use the image segmentation method to generate better adaptability. Mask, the spatial confidence map is obtained by solving the posterior probability; the posterior probability of the target is solved, as shown in the following formula:

[0074]

[0075] In the form...

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Abstract

The invention discloses an AI thermal imaging all-weather intelligent monitoring method, which comprises a control module and comprises the following steps: S1, extracting the working time of a systemby the control module, and starting a high-definition camera or a thermal imager according to a preset time threshold section H; S2, the control module obtains thermal imaging or high-definition camera video data, and transcodes thermal imaging AV format video output into video images; S3, performing motion detection on the video image by using a motion detection algorithm; Setting a remarkable motion detection threshold, and judging whether the current motion is remarkable or not, so as to filter out false detection caused by noise and light and shadow changes; Meanwhile, the detected movingtarget is subjected to screenshot; And S4, performing interested target segmentation on the detected video image with significant motion by using an instance segmentation algorithm to determine whether the target causing the current motion is a monitoring target interested in the system, and entering the next step if the segmentation result is the interested target.

Description

technical field [0001] The invention belongs to the field of thermal imaging and relates to an AI thermal imaging all-weather intelligent monitoring method. Background technique [0002] In recent years, due to the application of thermal imaging technology, the observation range and night vision ability of several times or dozens of times that of traditional monitoring equipment can be realized, and it has gradually been recognized by users. In 2018, my country deployed FOTRIC500 series thermal imaging night detection system. [0003] This framework uses an infrared thermal imager instead of a traditional high-definition camera and digital night vision equipment, which is not affected by the environment and can be monitored at any time during the day and night; the action distance is long (the longest monitoring distance can reach 5km); all-weather, multi-climate Conditions (it can also penetrate well in rainy and foggy days); fast tracking speed; wide coverage; stable and c...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/46G06K9/62G06T7/246G06T7/277
Inventor 易诗
Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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