CN116665120A - Tornado formation recognition and early warning system and method based on video - Google Patents
Tornado formation recognition and early warning system and method based on video Download PDFInfo
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- 238000012545 processing Methods 0.000 claims abstract description 31
- 235000001674 Agaricus brunnescens Nutrition 0.000 claims description 10
- 230000000877 morphologic effect Effects 0.000 claims description 6
- 238000003708 edge detection Methods 0.000 claims description 4
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/34—Smoothing or thinning of the pattern; Morphological operations; Skeletonisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
Abstract
The invention discloses a tornado formation recognition and early warning system and method based on video, which belong to the technical field of tornado recognition and early warning and comprise the following steps: the system comprises a video image acquisition module, a GMM model, a background subtraction module, a morphology processing module, a binarization processing module, a progressive pixel statistics module, a tornado identification module, a similar object removal module and a tornado alarm system. By means of the method, continuous sequence images are collected by utilizing the video cameras which are distributed throughout the places, the most recently formed tornado in the scene is identified through the image processing technology, the method has the characteristic of real-time monitoring, the problem that the tornado cannot be predicted by weather forecast can be solved, after the formed tornado is detected, sound or communication early warning can be sent to people nearby the tornado area in time, and life and property loss of people caused by the tornado can be reduced.
Description
Technical Field
The invention relates to the technical field of tornado identification and early warning, in particular to a video-based tornado formation identification and early warning system and method.
Background
With the continuous advancement of the construction of the skynet, video monitoring devices have become very popular, so that it is possible to acquire various abnormal conditions in the environment through video monitoring. The tornado is used as a short-time weather disaster, has the characteristics of short duration of minutes and long duration of hours and difficult prediction, can quickly acquire images formed by the tornado by utilizing video monitoring, can identify the tornado which is formed and is developing in real time through intelligent video identification, is combined with a communication network, can early warn people nearby a tornado area and on a travelling path, and reduces the life and property loss of people caused by the tornado.
Currently available tornado detection is mainly based on detection of air pressure, sound waves and the like by various radars and sensors. The Doppler radar is aligned with the microwave beam emitted by the tornado, and microwave signals are received by the radar after being reflected by chips and raindrops in the tornado. If the tornado moves away from the radar, the frequency of the reflected microwave signal moves towards the low frequency direction; conversely, if the tornado gets closer to the radar, the reflected signal will move in the high frequency direction. After receiving the signal, the radar operator can calculate the speed and moving direction of the tornado by analyzing the frequency shift data.
For example, chinese patent CN214201812U discloses a tornado detection system of ford corporation, which uses sensors such as doppler radar, lidar, accelerometer and gyroscope as a core to detect tornados near an automobile and infer the traveling direction of the tornados to remind the driver to avoid.
However, there is no solution for using video to identify tornado formation in the currently published literature. Based on the above, the invention designs a tornado formation identification and early warning system and method based on video to solve the above problems.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a tornado formation identification and early warning system and method based on video.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
a video-based tornado formation identification and early warning system, comprising:
the video image acquisition module is used for continuously acquiring monitoring images of the same scene through the video camera, so that consistency and continuity of monitoring backgrounds are ensured;
the GMM model is used for extracting a previous background image;
the background subtraction module is used for extracting a foreground target object based on the current frame and the background image;
the morphology processing module is used for carrying out morphology processing on the foreground target object and communicating the foreground object into a whole;
the binarization processing module is used for performing binarization processing on the foreground target object and converting the foreground target object into a black-white binary image;
the row-by-row pixel statistics module is used for sequentially counting the number of white pixels with pixel values of 1 in each row from top to bottom from the first row at the upper end of the foreground binary image, and storing the counting result of the number of the white pixels in each row into an image identification array according to the sequence from the small index to the large index;
the tornado identification module is used for identifying whether tornado exists or not based on the number of the white pixels of each row counted in the previous step;
the similar object removing module is used for removing objects with similar shapes to the tornadoes;
and the tornado alarm system is used for giving an early warning to the monitoring room when the tornado is detected.
Further, the background subtraction module performs subtraction operation by using the image acquired in the current video frame and the background image before 5 minutes extracted in the previous step, and acquires the foreground image after removing the background.
Further, after the background image is subtracted by the binarization processing module, a threshold delta is set, and when the gray value of the corresponding pixel after the background subtraction is smaller than the threshold delta, the pixel value is set to be 0 and is black; when the gray value of the corresponding pixel after background subtraction is larger than the threshold delta, setting the pixel value as 1 and displaying white;
such that:
further, the basic judgment standard of the tornado identification module is as follows: the white pixel areas are communicated into a whole, the image is in a mushroom shape or a funnel shape with big top and small bottom, and the number of white pixels counted by each row at the upper end of the image is 1.5 times greater than that of the white pixels below the image; the method is characterized in that the image identification array is characterized in that the value stored in the array has obvious value change from big to small, the value with smaller index is larger, the value with larger index is smaller, and the larger value is larger than 1.5 times of the smaller value.
Further, the objects of similar shape to a tornado are mainly mushroom-shaped clouds and funnel-shaped clouds; in order to avoid the two cloud types from being wrongly identified as tornados, whether the mushroom-shaped or funnel-shaped object at the upper end of the image is close to the ground or not is detected through an edge detection algorithm, and if the mushroom-shaped or funnel-shaped object is not close to the ground, the cloud type cloud is judged to be non-tornados.
Further, the similar object removing module detects according to the value of the image recognition array, if only elements smaller than 1/4 of the array length have continuous non-zero values in the array and the total number of the array elements with the continuous non-zero values is smaller than 1/3 of the array length, the formed mushroom cloud and funnel cloud are considered to be detected, and the system sends out a prompt to remind monitoring personnel to verify.
Further, after the tornado is identified to be formed, triggering a tornado alarm system to early warn a monitoring room; the monitoring personnel verify whether the tornado is actually the tornado, and after the tornado is confirmed to be formed, the tornado alarm is sent out again.
The invention also provides a tornado formation identification and early warning method based on video, which comprises the following steps:
1. continuously acquiring monitoring images of the same scene through a video camera of a video image acquisition module, and ensuring consistency and continuity of monitoring backgrounds;
2. extracting a background image before 5 minutes through a GMM model;
3. extracting a foreground target object based on the current frame and the background image by a background subtraction module;
4. carrying out morphological processing on the foreground target object through a morphological processing module and communicating the foreground object into a whole;
5. performing binarization processing on a foreground target object through a binarization processing module and converting the foreground target object into a black-white binary image;
6. sequentially counting the number of white pixels with pixel values of 1 in each row from top to bottom from the first row at the upper end of the foreground binary image through a row-by-row pixel counting module, and storing the counting result of the number of the white pixels in each row into an image identification array according to the sequence from small index to large index;
7. identifying whether the tornado is the tornado or not based on the number of the white pixels of each row counted in the previous step through a tornado identification module;
8. removing the object with the shape similar to that of the tornado through a similar object removing module;
9. and (5) early warning is carried out to a monitoring room when the tornado is detected through a tornado warning system.
Furthermore, the image acquired by the video camera of the video image acquisition module is based on the ground plane, and the picture of the part above the ground plane is not less than 80%.
Advantageous effects
The invention utilizes the video cameras distributed throughout the places at present to collect continuous sequence images, and identifies the tornado recently formed in the scene through an image processing technology, thereby having the characteristic of real-time monitoring, being capable of making up the difficult problem that the tornado can not be forecasted by weather forecast and having technical advancement;
according to the invention, the automatic identification of the tornado appearing in the monitoring image is realized by utilizing the sequential continuous images of the monitoring video and adopting image identification, and after the formed tornado is detected, sound or communication early warning can be timely sent to people nearby the tornado area, so that the life and property loss of people caused by the tornado is reduced.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Example 1
The embodiment provides a tornado formation recognition and early warning system based on video, which comprises:
the video image acquisition module is used for continuously acquiring monitoring images of the same scene through the video camera, so that consistency and continuity of monitoring backgrounds are ensured;
preferably, the image acquired by the video camera should take the ground plane as a reference, and the picture of the part above the ground plane is not less than 80%;
the GMM model is used for extracting background images before 5 minutes;
preferably, in this embodiment, a GMM (gaussian mixture background modeling) algorithm is used to extract a background image, and due to continuity of tornado formation, in order to dynamically reflect weather change conditions, and ensure a detection effect, an image used as a background subtraction is an extracted background image before 5 minutes, the background image may be obtained by using other background extraction algorithms, and a time interval between a current frame image and a background image may be adjusted according to conditions;
preferably, a Vibe algorithm and an interframe difference method can be adopted to extract the background image;
the background subtraction module is used for extracting a foreground target object based on the current frame and the background image;
preferably, the image obtained in the current video frame and the background image before 5 minutes extracted in the previous step are used for subtraction operation to obtain a foreground image after the background is removed, so as to obtain a foreground moving object newly appearing in the scene of the monitoring picture;
the morphology processing module is used for carrying out morphology processing on the foreground target object and communicating the foreground object into a whole;
preferably, noise interference in a foreground moving object is filtered, and holes in a foreground area are filled, so that foreground images are communicated into a whole;
the binarization processing module is used for performing binarization processing on the foreground target object and converting the foreground target object into a black-white binary image;
preferably, after the background image is subtracted by using a background subtraction method, the pixel gray value of the subtracted area of the background image is very small, a threshold delta is set, and when the corresponding pixel gray value after the background subtraction is smaller than the threshold delta, the pixel value is set to be 0 and is black; when the gray value of the corresponding pixel after background subtraction is larger than the threshold delta, setting the pixel value as 1 and displaying white;
such that:
the row-by-row pixel statistics module is used for sequentially counting the number of white pixels with pixel values of 1 in each row from top to bottom from the first row at the upper end of the foreground binary image, and storing the counting result of the number of the white pixels in each row into an image identification array according to the sequence from the small index to the large index;
the tornado identification module is used for identifying whether tornado exists or not based on the number of the white pixels of each row counted in the previous step;
preferably, the basic judgment criteria are: the white pixel areas are communicated into a whole, the image is in a mushroom shape or a funnel shape with big top and small bottom, and the number of white pixels counted by each row at the upper end of the image is 1.5 times greater than that of the white pixels below the image; the method is characterized in that the image identification array is characterized in that the value stored in the array has obvious change from big to small, the value with smaller index is larger, the value with larger index is smaller, and the larger value is larger than 1.5 times of the smaller value;
the similar object removing module is used for removing objects with similar shapes to the tornadoes;
preferably, the tornado-like shaped objects are mainly mushroom clouds and funnel clouds; in order to avoid the two cloud types from being wrongly identified as tornados, detecting whether a mushroom-shaped or funnel-shaped object at the upper end of the image is close to the ground or not through an edge detection algorithm, and if not, judging that the cloud type is not the tornados;
preferably, the detection is carried out according to the value of the image identification array, if only elements smaller than 1/4 of the array length have continuous non-zero values in the array and the total number of the array elements with the continuous non-zero values is smaller than 1/3 of the array length, the formed mushroom cloud and funnel cloud are considered to be detected, and the system sends out a prompt to remind monitoring personnel to verify;
the tornado alarm system is used for giving an early warning to the monitoring room when the tornado is detected;
preferably, if the tornado is identified to be formed, a tornado alarm is triggered, and the monitoring room is warned; in order to avoid the false alarm, a monitoring person can check whether the tornado is actually the tornado, and after the tornado is confirmed to be formed, the tornado alarm is sent out again, and the alarm mode can be various modes such as audio frequency, short message and the like.
Example 2
The embodiment provides a tornado formation identification and early warning method based on video, which comprises the following steps:
1. continuously acquiring monitoring images of the same scene through a video camera of a video image acquisition module, and ensuring consistency and continuity of monitoring backgrounds;
the image acquired by the video camera should take the ground plane as a reference, and the picture of the part above the ground plane is not less than 80%;
2. extracting a background image before 5 minutes through a GMM model;
in the embodiment, a GMM (Gaussian mixture background modeling) algorithm is adopted to extract a background image, and due to continuity of tornado formation, in order to dynamically reflect weather change conditions and ensure detection effects, the image used as background subtraction in the embodiment is an extracted background image before 5 minutes, the background image can be obtained by adopting other background extraction algorithms, and the time interval between a current frame image and a background image can be adjusted according to conditions;
the method can also adopt a Vite algorithm and an interframe difference method to extract background images;
3. extracting a foreground target object based on the current frame and the background image by a background subtraction module;
performing subtraction operation by using the image obtained in the current video frame and the background image before 5 minutes extracted in the previous step to obtain a foreground image after removing the background, so as to obtain a foreground moving object newly appearing in the scene of the monitoring picture;
4. carrying out morphological processing on the foreground target object through a morphological processing module and communicating the foreground object into a whole;
filtering noise interference in a foreground moving object, filling holes in a foreground region, and communicating foreground images into a whole;
5. performing binarization processing on a foreground target object through a binarization processing module and converting the foreground target object into a black-white binary image;
after the background image is subtracted by using a background subtraction method, the pixel gray value of the subtracted area of the background image is small, a threshold delta is set, and when the corresponding pixel gray value after the background subtraction is smaller than the threshold delta, the pixel value is set to be 0 and is black; when the gray value of the corresponding pixel after background subtraction is larger than the threshold delta, setting the pixel value as 1 and displaying white;
such that:
6. sequentially counting the number of white pixels with pixel values of 1 in each row from top to bottom from the first row at the upper end of the foreground binary image through a row-by-row pixel counting module, and storing the counting result of the number of the white pixels in each row into an image identification array according to the sequence from small index to large index;
7. identifying whether the tornado is the tornado or not based on the number of the white pixels of each row counted in the previous step through a tornado identification module;
the basic judgment standard is as follows: the white pixel areas are communicated into a whole, the image is in a mushroom shape or a funnel shape with big top and small bottom, and the number of white pixels counted by each row at the upper end of the image is 1.5 times greater than that of the white pixels below the image; the method is characterized in that the image identification array is characterized in that the value stored in the array has obvious change from big to small, the value with smaller index is larger, the value with larger index is smaller, and the larger value is larger than 1.5 times of the smaller value;
8. removing the object with the shape similar to that of the tornado through a similar object removing module;
objects of similar shape to tornadoes are mainly mushroom clouds and funnel clouds; in order to avoid the two cloud types from being wrongly identified as tornados, detecting whether a mushroom-shaped or funnel-shaped object at the upper end of the image is close to the ground or not through an edge detection algorithm, and if not, judging that the cloud type is not the tornados;
detecting according to the value of the image identification array, if only elements smaller than 1/4 of the array length have continuous non-zero values in the array and the total number of the array elements with the continuous non-zero values is smaller than 1/3 of the array length, the formed mushroom cloud and funnel cloud are considered to be detected, and the system sends out a prompt to remind monitoring personnel to verify;
9. warning a monitoring room when the tornado is detected through a tornado warning system;
triggering tornado alarm and early warning to a monitoring room after the tornado is identified to be formed;
in order to avoid the false alarm, a monitoring person can check whether the tornado is actually the tornado, and after the tornado is confirmed to be formed, the tornado alarm is sent out again, and the alarm mode can be various modes such as audio frequency, short message and the like.
The invention utilizes the video cameras distributed throughout the places at present to collect continuous sequence images, and identifies the tornado recently formed in the scene through an image processing technology, thereby having the characteristic of real-time monitoring, being capable of making up the difficult problem that the tornado can not be forecasted by weather forecast and having technical advancement;
according to the invention, the automatic identification of the tornado appearing in the monitoring image is realized by utilizing the sequential continuous images of the monitoring video and adopting image identification, and after the formed tornado is detected, sound or communication early warning can be timely sent to people nearby the tornado area, so that the life and property loss of people caused by the tornado is reduced.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A video-based tornado formation identification and early warning system, comprising:
the video image acquisition module is used for continuously acquiring monitoring images of the same scene through the video camera, so that consistency and continuity of monitoring backgrounds are ensured;
the GMM model is used for extracting a previous background image;
the background subtraction module is used for extracting a foreground target object based on the current frame and the background image;
the morphology processing module is used for carrying out morphology processing on the foreground target object and communicating the foreground object into a whole;
the binarization processing module is used for performing binarization processing on the foreground target object and converting the foreground target object into a black-white binary image;
the row-by-row pixel statistics module is used for sequentially counting the number of white pixels with pixel values of 1 in each row from top to bottom from the first row at the upper end of the foreground binary image, and storing the counting result of the number of the white pixels in each row into an image identification array according to the sequence from the small index to the large index;
the tornado identification module is used for identifying whether tornado exists or not based on the number of the white pixels of each row counted in the previous step;
the similar object removing module is used for removing objects with similar shapes to the tornadoes;
and the tornado alarm system is used for giving an early warning to the monitoring room when the tornado is detected.
2. The video-based tornado formation recognition and early warning system according to claim 1, wherein the background subtraction module performs subtraction operation using the image acquired in the current video frame and the background image 5 minutes ago extracted in the previous step, and acquires the foreground image after removing the background.
3. The video-based tornado formation recognition and early warning system according to claim 2, wherein the binarization processing module uses a background subtraction method to subtract the background image, and then sets a threshold value delta, and sets the pixel value to 0 and black when the gray value of the corresponding pixel after the background subtraction is smaller than the threshold value delta; when the gray value of the corresponding pixel after background subtraction is larger than the threshold delta, setting the pixel value as 1 and displaying white;
such that:
4. the video-based tornado formation identification and pre-warning system of claim 3, wherein the basic criteria of the tornado identification module are: the white pixel areas are communicated into a whole, the image is in a mushroom shape or a funnel shape with big top and small bottom, and the number of white pixels counted by each row at the upper end of the image is 1.5 times greater than that of the white pixels below the image; the method is characterized in that the image identification array is characterized in that the value stored in the array has obvious value change from big to small, the value with smaller index is larger, the value with larger index is smaller, and the larger value is larger than 1.5 times of the smaller value.
5. The video-based tornado formation identification and pre-warning system of claim 4, wherein the tornado-shaped objects are primarily mushroom clouds and funnel clouds; in order to avoid the two cloud types from being wrongly identified as tornados, whether the mushroom-shaped or funnel-shaped object at the upper end of the image is close to the ground or not is detected through an edge detection algorithm, and if the mushroom-shaped or funnel-shaped object is not close to the ground, the cloud type cloud is judged to be non-tornados.
6. The system of claim 5, wherein the similar object removal module detects the value of the image recognition array, and if only elements less than 1/4 of the array length have continuous non-zero values and the total number of array elements with continuous non-zero values is less than 1/3 of the array length, the system sends a prompt to remind a monitoring person to verify.
7. The video-based tornado formation recognition and early warning system of claim 6, wherein the tornado warning system is triggered to early warn a monitoring room if a tornado is recognized as being formed; the monitoring personnel verify whether the tornado is actually the tornado, and after the tornado is confirmed to be formed, the tornado alarm is sent out again.
8. The method for video-based tornado formation identification and pre-warning of claim 7, comprising the steps of:
1. continuously acquiring monitoring images of the same scene through a video camera of a video image acquisition module, and ensuring consistency and continuity of monitoring backgrounds;
2. extracting a previous background image through a GMM model;
3. extracting a foreground target object based on the current frame and the background image by a background subtraction module;
4. carrying out morphological processing on the foreground target object through a morphological processing module and communicating the foreground object into a whole;
5. performing binarization processing on a foreground target object through a binarization processing module and converting the foreground target object into a black-white binary image;
6. sequentially counting the number of white pixels with pixel values of 1 in each row from top to bottom from the first row at the upper end of the foreground binary image through a row-by-row pixel counting module, and storing the counting result of the number of the white pixels in each row into an image identification array according to the sequence from small index to large index;
7. identifying whether the tornado is the tornado or not based on the number of the white pixels of each row counted in the previous step through a tornado identification module;
8. removing the object with the shape similar to that of the tornado through a similar object removing module;
9. and (5) early warning is carried out to a monitoring room when the tornado is detected through a tornado warning system.
9. The method for identifying and pre-warning tornado formation based on video according to claim 8, wherein the image acquired by the video camera of the video image acquisition module is based on a ground plane, and the number of pictures above the ground plane is not less than 80%.
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| CN117111068A (en) * | 2023-10-19 | 2023-11-24 | 南京信大卫星应用研究院有限公司 | Sea surface wind field monitoring system based on satellite scatterometer data |
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| CN117111068A (en) * | 2023-10-19 | 2023-11-24 | 南京信大卫星应用研究院有限公司 | Sea surface wind field monitoring system based on satellite scatterometer data |
| CN117111068B (en) * | 2023-10-19 | 2024-03-22 | 南京信大卫星应用研究院有限公司 | Sea surface wind field monitoring system based on satellite scatterometer data |
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