CN115456868B - Data management method for fire drill system - Google Patents
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Abstract
The invention relates to the technical field of electric digital data processing, in particular to a data management method of a fire drill system, which comprises the following steps: acquiring each frame of gray level image corresponding to a fire drill site; calculating the gray threshold of each frame of gray image, and then obtaining core pixel points; acquiring each area of each frame of gray level image according to the core pixel points; calculating the position label of each region, and then matching each region in two adjacent frames of gray level images to obtain each matching pair; for any matching pair, calculating the amount of loss information of the corresponding area of the next frame of gray level image in the matching pair; for two adjacent frames of gray level images, calculating the integral loss information quantity of the next frame of gray level image according to the loss information quantity corresponding to all the areas in the next frame of gray level image, and judging whether the next frame of gray level image is reserved or not; and compressing and transmitting the video images corresponding to all the reserved gray level images. The invention can reduce the data volume of video image compression and transmission.
Description
Technical Field
The invention relates to the technical field of electric digital data processing, in particular to a data management method of a fire drill system.
Background
The fire drill system is a simulation system specially applied to fire drill in recent years, namely, video data of a fire drill site is collected, the video data of the fire drill site is transmitted to a terminal by using a transmission mode of the fire drill system, and then a terminal monitoring person assists the site drill person to perform fire drill. In a fire drill system, the instantaneity of live video data transmission often has a great influence on the drill result.
In the conventional video instant transmission technology, the original video data is compressed by using predictive coding so as to reduce the transmission size of the video data, and for the video transmission in fire drilling, the problem of insufficient transmission efficiency, overlong transmission time and lost data real-time property can be caused if the amount of the video data needing to be transmitted in the emergency scene is too large; by observing the video data of the fire drilling site collected by the fire drilling system, the fact that the video data of the fire drilling site transmitted by the conventional video instant transmission technology has considerable redundancy is easily found, namely, the information carried between the video data corresponding to continuous multi-frame video images is almost the same, so that the size of the transmitted data volume is increased by compressing and transmitting all the video data, and the transmission efficiency of the video data of the fire drilling site is reduced.
Therefore, there is a need for an efficient method to achieve accurate transmission of video data at the site of fire fighting exercise.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a fire drill system data management method, which adopts the following technical scheme:
acquiring video image data corresponding to a fire drill site, wherein the video image data comprises at least two frames of video images; preprocessing each frame of video image to obtain each frame of gray level image;
performing curve fitting on the gray histogram corresponding to each frame of gray image to obtain a peak-valley curve corresponding to each frame of gray image, and calculating the gray threshold of each frame of gray image according to the gray value corresponding to each peak in the peak-valley curve; selecting core pixel points corresponding to the gray level images of each frame according to the gray level threshold;
acquiring at least two areas corresponding to each frame of gray level image according to the core pixel points; calculating the position label of each region according to the horizontal and vertical coordinate values corresponding to each pixel point in the region; matching each area in two adjacent frames of gray level images according to the position labels to obtain at least two matching pairs; there are two regions in the matching pair;
for any matching pair, calculating the average gray value and the variance corresponding to each region according to the gray values corresponding to all pixel points in each region in the matching pair, and further obtaining the loss information quantity of the region corresponding to the next frame of gray image in the matching pair;
for two adjacent frames of gray level images, calculating the integral loss available information amount corresponding to the next frame of gray level image according to the loss available information amount corresponding to all the areas in the next frame of gray level image, the image entropy and the average gray level value corresponding to the next frame of gray level image and the image entropy and the average gray level value corresponding to the previous frame of gray level image; judging whether to reserve the next frame of gray level image according to the whole loss information amount;
and compressing and transmitting the video images corresponding to all the retained gray level images.
Preferably, the method for calculating the gray threshold of each frame of gray image according to the gray value corresponding to each peak in the peak-valley curve comprises: and calculating the average gray value corresponding to all the wave crests according to the gray value corresponding to each wave crest in the peak-valley curve, and recording the average gray value corresponding to all the wave crests as the gray threshold of the corresponding frame gray image.
Preferably, the method for selecting the core pixel point corresponding to each frame of gray scale image according to the gray scale threshold value comprises the following steps: comparing the gray threshold value with the judgment threshold value for any frame of gray image, and recording the pixel points corresponding to the gray values smaller than the gray threshold value as core pixel points corresponding to the frame of gray image when the gray threshold value is larger than the judgment threshold value; and when the gray threshold is less than or equal to the judgment threshold, marking the pixel points corresponding to the gray threshold with the gray value greater than or equal to the gray threshold as core pixel points corresponding to the frame of gray image.
Preferably, the method for obtaining at least two regions corresponding to each frame of gray-scale image according to the core pixel point comprises: and acquiring at least two regions corresponding to each frame of gray level image by using a region growing algorithm according to the core pixel points.
Preferably, the method for calculating the position label of each region according to the horizontal and vertical coordinate values corresponding to each pixel point in the region comprises: and calculating average abscissa values and average ordinate values corresponding to all pixel points in the region, wherein the average abscissa values and the average ordinate values form a position label.
Preferably, the method for matching each region in two adjacent frames of gray images according to the position tag to obtain at least two matching pairs comprises: and matching each region in the two adjacent frames of gray images by using a KM algorithm according to the position tags to obtain at least two matching pairs.
Preferably, the amount of information that can be lost is:
wherein,when the next frame of gray image in any matching pair is the nth frame of gray image, the amount of information which can be lost in the kth area in the nth frame of gray image is reduced;is a natural constant;in the formula (I), wherein,the average gray value corresponding to the kth area in the nth frame gray image;for the (n-1) th frame in the grayscale imageAverage gray values corresponding to the areas;the sign of the absolute value is calculated;in the formula (I), wherein,the variance corresponding to the kth area in the nth frame gray level image;for the (n-1) th frame in the grayscale imageThe variance corresponding to each region;the sign of the absolute value is calculated.
Preferably, the total amount of information that can be lost is:
wherein,when the next frame of gray image in the two adjacent frames of gray images is the nth frame of gray image, the whole amount of information corresponding to the nth frame of gray image can be lost;the amount of information which can be lost in the kth area in the nth frame gray level image;the total number of the areas in the nth frame gray level image;the image entropy corresponding to the nth frame of gray level image is obtained;the image entropy corresponding to the (n-1) th frame gray level image;the average gray value corresponding to the n-th frame of gray image;the average gray value corresponding to the gray image of the (n-1) th frame;is a natural constant.
Preferably, the method for determining whether to retain the gray image of the next frame according to the total amount of information that can be lost comprises: normalizing the integral loss-able information quantity, and comparing the normalized integral loss-able information quantity with a threshold value; when the normalized integral loss information quantity is larger than a threshold value, deleting the next frame of gray level image; and when the normalized integral loss information amount is less than or equal to the threshold value, retaining the next frame of gray image.
The embodiment of the invention at least has the following beneficial effects:
the method comprises the steps of calculating the integral loss information quantity of each frame of gray level image corresponding to video image data, judging whether the frame of gray level image is reserved or not according to the integral loss information quantity of each frame of gray level image, realizing accurate selection of the video image corresponding to a fire drilling site, judging that the two adjacent frames of gray level images are highly similar when the integral loss information quantity corresponding to the next frame of gray level image in the two adjacent frames of gray level images is larger, and deleting the next frame of gray level image; the method and the device provided by the invention have the advantages that the information contained in the video image corresponding to the fire drill site is complete, the data volume of the video image data for compression transmission is reduced, the transmission efficiency of the video image data of the fire drill site is improved, and the instantaneity of transmission is ensured.
Meanwhile, for any one matching pair, according to the gray values corresponding to all pixel points in each region in the matching pair, calculating the average gray value and the variance corresponding to each region, and further obtaining the loss information quantity of the region corresponding to the next frame of gray image in the matching pair; in the calculation of the amount of information which can be lost, not only the average gray value corresponding to the region but also the variance corresponding to the region are considered, and the calculation is performed only through the average gray value corresponding to the region, so that the method has great contingency; thus, the variance is used to reduce this contingency and improve the accuracy with which the amount of information can be lost.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart illustrating steps of a data management method for a fire drill system according to an embodiment of the present invention.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the proposed solution, its specific implementation, structure, characteristics and effects will be given in conjunction with the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The main purposes of the invention are: preprocessing each frame of video image to be transmitted in the fire fighting drilling system to obtain a gray image; further carrying out region division on each frame of gray level image to obtain at least two regions corresponding to each frame of gray level image; then, carrying out area matching on two adjacent frames of gray level images to obtain at least two matching pairs; calculating the amount of loss information of the corresponding area of the gray level image of the next frame in the matching pair; further obtaining the whole loss-capable information amount corresponding to the next frame of gray level image; judging whether to reserve the next frame of gray level image according to the whole loss information quantity; and compressing and transmitting the video images corresponding to all the retained gray level images. According to the invention, one highly similar two-frame video image is deleted through the whole lost information quantity, so that the number of video images during video image data transmission is reduced; the transmission efficiency is improved.
Referring to fig. 1, a flowchart illustrating steps of a fire drill system data management method according to an embodiment of the present invention is shown, where the method includes the following steps:
step 1, acquiring video image data corresponding to a fire drill site, wherein the video image data comprises at least two frames of video images; and preprocessing each frame of video image to obtain each frame of gray level image.
Specifically, a field worker shoots a fire drill field in real time through a camera to obtain video image data corresponding to the fire drill field; each shot frame of video image is an RGB image; the RGB image is a three-channel image, and the three channels are an R channel, a G channel and a B channel respectively; if each frame of the shot video image is directly used, the calculation amount of the subsequent process is increased, so that the embodiment performs graying processing on each frame of the video image to obtain each frame of the gray image; the gray image is a single-channel image, and the calculation amount can be reduced in the subsequent calculation process.
The graying method is known technology and will not be described; the implementer can select a corresponding graying method to process each frame of video image.
Step 2, performing curve fitting on a gray histogram corresponding to each frame of gray image to obtain a peak-valley curve corresponding to each frame of gray image, and calculating a gray threshold of each frame of gray image according to a gray value corresponding to each peak in the peak-valley curve; and selecting core pixel points corresponding to the gray level images of each frame according to the gray level threshold value.
The gray histogram corresponding to each frame of gray image is obtained according to the gray value of the pixel point in each frame of gray image, the gray histogram represents the number of the pixel points with certain gray value in the gray image, the frequency of the occurrence of certain gray value in the gray image is reflected, and the obtaining process is a known technology and is not repeated.
Then, curve fitting is carried out on the gray level histogram by utilizing an envelope fitting technology of EMD to obtain a peak-valley curve corresponding to each frame of gray level image; because the gray level image is obtained according to the video image corresponding to the fire drill site, and the environment of the fire drill site is extremely complex, the distribution of the gray level values corresponding to the pixel points in the gray level image is also extremely complex, so that the peak-valley curve corresponding to each frame of gray level image presents a form of fluctuation, and has a plurality of peaks and a plurality of valleys. The envelope fitting technique of EMD is a known technique and will not be described in detail.
Calculating the gray threshold of each frame of gray image according to the gray value corresponding to each peak in the peak-valley curve; specifically, according to the gray value corresponding to each peak in the peak-valley curve, the average gray value corresponding to all peaks is calculated, the average gray value corresponding to all peaks is recorded as the gray threshold of the corresponding frame gray image, and the formula is expressed as follows:
in the formula,the gray threshold value is the gray threshold value of the nth frame of gray image;the peak-valley curve corresponding to the n-th frame of gray scale imageGray values corresponding to the individual wave crests;the total number of peaks in a peak-valley curve corresponding to the nth frame of gray scale image.
The gray value corresponding to each peak in the peak-valley curve corresponding to the n-th frame of gray image represents that the number of pixel points corresponding to the gray value in the n-th frame of gray image is large; therefore, the average gray value corresponding to all the peaks in each peak-valley curve is recorded as the gray threshold of the corresponding frame gray image, and the larger the gray threshold is, the more the peaks near the larger gray value in the corresponding frame gray image are; most of the pixels are distributed near the larger gray value, and because the edge pixels have larger difference with other pixels in the gray image, the probability that the pixels corresponding to the smaller gray value are the edge pixels is higher; the smaller the gray threshold value is, the more wave peaks near the smaller gray value in the corresponding frame gray image are indicated; that is, most of the pixels are distributed near the smaller gray value, and the edge pixel point has a larger difference from other pixels in the gray image, so that the probability that the pixel corresponding to the larger gray value is the edge pixel point is higher.
Finally, selecting core pixel points corresponding to the gray level images of each frame according to the gray level threshold value; comparing the gray threshold value with the judgment threshold value for any frame of gray image, and recording the pixel points corresponding to the gray values smaller than the gray threshold value as core pixel points corresponding to the frame of gray image when the gray threshold value is larger than the judgment threshold value; and when the gray threshold is less than or equal to the judgment threshold, marking the pixel points corresponding to the gray threshold with the gray value greater than or equal to the gray threshold as core pixel points corresponding to the frame of gray image. In this embodiment, the value of the determination threshold is 125, and an implementer can adjust the value of the determination threshold according to actual conditions.
It should be noted that, because the edge pixel points can reflect the information contained in the gray level image, and when the gray level threshold is greater than the determination threshold, the probability that the pixel points corresponding to the gray level value less than the gray level threshold are the edge pixel points is higher, the pixel points corresponding to the gray level value less than the gray level threshold are marked as the core pixel points of the corresponding frame gray level image; when the gray threshold is less than or equal to the judgment threshold, the probability that the pixel points corresponding to the gray values greater than or equal to the gray threshold are edge pixel points is high, and therefore the pixel points corresponding to the gray values greater than or equal to the gray threshold are marked as core pixel points of the corresponding frame gray image.
Step 3, acquiring at least two areas corresponding to each frame of gray level image according to the core pixel points; calculating the position label of each region according to the horizontal and vertical coordinate values corresponding to each pixel point in the region; matching each area in two adjacent frames of gray level images according to the position labels to obtain at least two matching pairs; there are two regions in the matched pair.
Specifically, according to core pixel points, at least two regions corresponding to each frame of gray level image are obtained by using a region growing algorithm; the region growing algorithm is a known technique, is not in the protection scope of the present invention, and is not described in detail.
Further, calculating the position label of each region according to the horizontal and vertical coordinate values corresponding to each pixel point in the region; namely, calculating the average abscissa value and the average ordinate value corresponding to all the pixel points in the region, wherein the average abscissa value and the average ordinate value form a position tag. The horizontal and vertical coordinate values corresponding to each pixel point in the area are obtained through an image coordinate system corresponding to the frame gray level image; the establishment of the image coordinate system is a known technique and is not described in detail.
Taking the kth area in the n-th frame gray image as an example, the method for acquiring the position label is described, that is, the position label of the kth area in the n-th frame gray image isWhereinin the formula (I), wherein,is as followsIn the kth region in the frame gray imageThe abscissa value of each pixel point;is as followsThe total number of pixel points in the kth region in the frame gray image;in the formula (I), the reaction is carried out,is as followsIn the kth region in the frame gray imageThe longitudinal coordinate value of each pixel point;is as followsThe total number of pixel points in the kth region in the frame gray image.
When the regions in two adjacent frames of gray scale images are subsequently matched according to the position tags, because the positions of the same information on different frames of gray scale images may be different, the embodiment utilizes the second embodimentAverage abscissa values and average ordinate values corresponding to all pixel points in a kth region in a frame gray image are used as position labels of the kth region, matching between the regions in two adjacent frames of gray images is carried out through the position labels, the more the number of the pixel points in the regions is, the larger the fault tolerance rate of the calculated position labels is, and the higher the matching accuracy between the regions is in the subsequent matching.
Obtaining position labels corresponding to all areas in each frame of gray level image by the general method; matching each area in two adjacent frames of gray level images according to the position labels; the specific matching mode is to take the n frame gray image and the n-1 frame gray image as an example, and the KM algorithm is utilized to match the n frame gray image and the n-1 frame gray image according to the position labelMatching the frame gray level image with each region in the n-1 th frame gray level image to obtain a matching pair; the KM algorithm is a known technique, and is not within the protection scope of the present invention, and the specific matching process is not described.
And 4, for any matching pair, calculating the average gray value and the variance corresponding to each region according to the gray values corresponding to all the pixel points in each region in the matching pair, and further obtaining the amount of information which can be lost in the region corresponding to the next frame of gray image in the matching pair.
The amount of information that can be lost is:
wherein,when the next frame of gray image in any matching pair is the nth frame of gray image, the amount of information which can be lost in the kth area in the nth frame of gray image is reduced;is a natural constant;in the formula (I), wherein,the average gray value corresponding to the kth area in the nth frame gray image;for the (n-1) th frame in the grayscale imageAverage gray values corresponding to the regions;calculating the sign of the absolute value;in the formula (I), wherein,variance corresponding to k-th area in n-th frame gray image;For the (n-1) th frame in the grayscale imageThe variance corresponding to each region;the sign of the absolute value is calculated.
It should be noted that the kth region in the n-th frame gray image and the kth region in the n-1-th frame gray imageEach region forms a matching pair; in each frame of gray scale image corresponding to video image data of the fire drill site, if the information quantity carried by the kth area in the nth frame of gray scale image is equal to that of the kth areaIn the frame gray imageUnder the condition that the information quantity carried by each region is not much different, the difference of the average gray values corresponding to all the pixel points in the two regions is not big, and the difference of the variances corresponding to all the pixel points in the two regions is not big;representing the difference of the average gray values corresponding to all the pixel points in the two regions,the variance difference corresponding to all pixel points in the two regions is represented, so thatIs less valued than inWhen the value is small, the description will be givenIn gray scale images in framesAn area and aIn the frame gray imageThe information change condition corresponding to each area is not large, which shows that even the first area isIn the frame gray imageThe loss of information from a region is acceptable with respect to video image data because of the availability of the second regionIn the frame gray imageA region replaces the firstIn the frame gray imageAn area; otherwise, it is impossible to useIn the frame gray imageA region replaces the firstIn the frame gray imageAnd (4) a region.
The reason why the quantization is performed by using the product of the difference between the variances and the difference between the average gray-scale values in the calculation of the amount of information that can be lost is that although the average gray-scale value can represent the approximate range of the gray-scale values of all the pixels in the two regions, the contingency of the average gray-scale value is too strong, for example, if there are only two pixels in each region in the matching pair, the gray-scale values of the two pixels in one region are respectively the gray-scale values of the two pixels in one region(ii) a The gray values of two pixel points in the other area are respectively(ii) a Although the corresponding average gray values are equal, the information contained in the two areas actually has extremely large difference, so that the difference of the variance is utilized to carry out constraint to reduce the contingency, and then the negative exponential function of a natural constant e is utilized to carry out inversion to obtain a loss-capable information quantity which is used for representing the loss-capable degree of the information in the corresponding area; when it comes toIn the frame gray imageAn area and aIn the frame gray imageInformation carried by an area becomesThe smaller the chemical change, the smaller theThe greater the value of (A), i.e. the firstIn the frame gray imageThe higher the extent to which information in an area can be lost.
Step 5, for two adjacent frames of gray images, calculating the integral loss information amount corresponding to the next frame of gray images according to the loss information amount corresponding to all the areas in the next frame of gray images, the image entropy and the average gray value corresponding to the next frame of gray images and the image entropy and the average gray value corresponding to the previous frame of gray images; and judging whether to reserve the gray image of the next frame or not according to the whole loss information quantity.
The total amount of information that can be lost is:
wherein,when the next frame of gray image in the two adjacent frames of gray images is the nth frame of gray image, the whole amount of information which can be lost is corresponding to the nth frame of gray image;the amount of information which can be lost in the kth area in the nth frame gray level image;the total number of the areas in the nth frame gray level image;the image entropy corresponding to the nth frame gray level image;the image entropy corresponding to the gray level image of the (n-1) th frame is obtained;the average gray value corresponding to the nth frame of gray image is obtained;the average gray value corresponding to the gray image of the (n-1) th frame;is a natural constant. The calculation method of the image entropy is a known technology and is not described in detail.
First, theThe total amount of information that can be lost corresponding to the frame gray image is composed of two parts, the first partThe average amount of information loss of the regions corresponding to all the regions in the frame gray level imageOf 1 atFrame gray scale image andinter-frame loss-able information amount of frame gray image(ii) a Area average loss-capable information amount representationEach region in the frame gray image corresponds toFor each region in the frame gray image, the firstThe extent of loss in each region in the frame gray image is described as follows, the greater the extent of lossEach region in the frame gray image and the secondThe information quantity carried by each area in the frame gray image is not very different, namelyThe regions in the frame gray image may be selected fromThe regions in the frame gray image.
While the amount of information between frames can be lostFrom the firstImage entropy corresponding to frame gray level imageRatio of image entropies corresponding to the frame gray image, secondAverage gray value corresponding to frame gray image and the second gray valueThe ratio of the average gray values corresponding to the frame gray image and the product of the two ratios are formedFrame gray scale image andthe closer the information carried by the frame gray images is, the closer the amount of information that can be lost between frames is to 1, because the closer the information carried by the two frame gray images is, the smaller the difference between the two frame gray images is, i.e. the closer the average gray values corresponding to the two frame gray images are,the closer to 1; the more similar the distribution of the gray values corresponding to all the pixel points in the two frames of gray images, the closer the corresponding image entropy is,the closer to 1, theThe closer to 0, the correspondingThe closer to 1;the larger the value of (A), the more the description is aboutLoss of frame gray level image, and elegance and no damage compared with video image data corresponding to fire drill siteThe frame gray image can be completely composed ofThe frame gray image is replaced.
Further, whether a next frame gray image in two adjacent frames of gray images is reserved or not is judged according to the whole loss information quantity; normalizing the integral loss-able information quantity, and comparing the normalized integral loss-able information quantity with a threshold value; when the normalized integral loss information quantity is larger than a threshold value, deleting the next frame of gray level image; when the normalized integral loss information amount is less than or equal to the threshold value, the gray level image of the next frame is reserved; in this embodiment, the value of the threshold is 0.7, and an implementer can adjust the value of the threshold according to actual conditions in a specific operation process.
It should be noted that, taking two adjacent frames of gray scale images as the nth frame of gray scale image and the (n-1) th frame of gray scale image as an example, when the normalized total loss-enabled information amount is greater than the threshold value, that is, when the normalized total loss-enabled information amount corresponding to the nth frame of gray scale image is greater than the threshold value, at this time, it is considered that the nth frame of gray scale image is the first frame of gray scale imageFrame gray scale image andthe frame gray level images are highly similarThe frame gray image can be completely composed ofAnd replacing the frame gray image and reserving one frame gray image.
And 6, compressing and transmitting the video images corresponding to all the retained gray level images.
Specifically, the video images corresponding to all the reserved gray level images are compressed by using the predictive coding, the predictive coding selects backward prediction, the calculated amount is small, and then the compressed video images are transmitted by using a transmission mode of a fire drilling system.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; the modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application, and are included in the protection scope of the present application.
Claims (5)
1. A fire drill system data management method is characterized by comprising the following steps:
acquiring video image data corresponding to a fire drill site, wherein the video image data comprises at least two frames of video images; preprocessing each frame of video image to obtain each frame of gray level image;
performing curve fitting on the gray histogram corresponding to each frame of gray image to obtain a peak-valley curve corresponding to each frame of gray image, and calculating the gray threshold of each frame of gray image according to the gray value corresponding to each peak in the peak-valley curve; selecting core pixel points corresponding to the gray level images of each frame according to the gray level threshold;
acquiring at least two areas corresponding to each frame of gray level image according to the core pixel points; calculating the position label of each region according to the horizontal and vertical coordinate values corresponding to each pixel point in the region; matching each area in two adjacent frames of gray level images according to the position labels to obtain at least two matching pairs; there are two regions in the matching pair;
for any matching pair, calculating the average gray value and the variance corresponding to each region according to the gray values corresponding to all the pixel points in each region in the matching pair, and further obtaining the amount of information which can be lost in the region corresponding to the next frame of gray image in the matching pair;
for two adjacent frames of gray level images, calculating the integral loss available information amount corresponding to the next frame of gray level image according to the loss available information amount corresponding to all the areas in the next frame of gray level image, the image entropy and the average gray level value corresponding to the next frame of gray level image and the image entropy and the average gray level value corresponding to the previous frame of gray level image; judging whether to reserve the next frame of gray level image according to the whole loss information quantity;
compressing and transmitting the video images corresponding to all the reserved gray level images;
the method for calculating the gray threshold of each frame of gray image according to the gray value corresponding to each peak in the peak-valley curve comprises the following steps: calculating the average gray value corresponding to all the wave crests according to the gray value corresponding to each wave crest in the peak-valley curve, and recording the average gray value corresponding to all the wave crests as the gray threshold of the corresponding frame gray image;
the method for selecting the core pixel point corresponding to each frame of gray level image according to the gray level threshold value comprises the following steps: comparing the gray threshold value with the judgment threshold value for any frame of gray image, and recording the pixel points corresponding to the gray values smaller than the gray threshold value as core pixel points corresponding to the frame of gray image when the gray threshold value is larger than the judgment threshold value; when the gray threshold is less than or equal to the judgment threshold, marking the pixel points corresponding to the gray values greater than or equal to the gray threshold as core pixel points corresponding to the frame of gray image;
the method for acquiring at least two regions corresponding to each frame of gray level image according to the core pixel points comprises the following steps: acquiring at least two regions corresponding to each frame of gray level image by using a region growing algorithm according to the core pixel points;
the total amount of information that can be lost is:
wherein,when the next frame of gray image in the two adjacent frames of gray images is the nth frame of gray image, the whole amount of information which can be lost is corresponding to the nth frame of gray image;the amount of information which can be lost in the kth area in the nth frame gray level image;the total number of the areas in the nth frame gray level image;the image entropy corresponding to the nth frame gray level image;the image entropy corresponding to the (n-1) th frame gray level image;the average gray value corresponding to the nth frame of gray image is obtained;the average gray value corresponding to the gray image of the (n-1) th frame;are natural constants.
2. The fire drill system data management method according to claim 1, wherein the method for calculating the position label of each area according to the abscissa and ordinate values corresponding to each pixel point in each area comprises: and calculating average abscissa values and average ordinate values corresponding to all pixel points in the region, wherein the average abscissa values and the average ordinate values form a position label.
3. The fire drill system data management method according to claim 1, wherein the method for matching each region in two adjacent frames of gray images according to the position tags to obtain at least two matching pairs comprises: and matching each region in the two adjacent frames of gray images by using a KM algorithm according to the position tags to obtain at least two matching pairs.
4. The fire drill system data management method according to claim 1, wherein the amount of losable information is:
wherein,when the next frame of gray image in any matching pair is the nth frame of gray image, the amount of information which can be lost in the kth area in the nth frame of gray image is reduced;is a natural constant;in the formula (I), wherein,the average gray value corresponding to the kth area in the nth frame gray image;for the (n-1) th frame in the grayscale imageAverage gray values corresponding to the regions;calculating the sign of the absolute value;in the formula (I), wherein,for the kth region pair in the n-th frame gray imageThe corresponding variance;for the (n-1) th frame in the grayscale imageThe variance corresponding to each region;the sign of the absolute value is calculated.
5. The fire drill system data management method according to claim 1, wherein the method for determining whether to retain the next frame of gray scale image according to the total amount of losable information comprises: normalizing the integral loss-able information quantity, and comparing the normalized integral loss-able information quantity with a threshold value; when the normalized integral loss information quantity is larger than a threshold value, deleting the next frame of gray level image; and when the normalized integral loss information amount is less than or equal to the threshold value, the gray level image of the next frame is reserved.
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