Elevator monitoring video static picture recognition system and method based on SSIM algorithm

Abstract

The invention discloses an elevator monitoring video static picture recognition system and method based on an SSIM algorithm, and the system comprises a video collection module which collects monitoring video data in an elevator, and transmits the monitoring video data to a preprocessing module; the preprocessing module is used for preprocessing the monitoring video data to obtain target video data; the video processing module is used for processing the target video data through an SSIM algorithm to obtain inter-frame similarity of the target video data; the static picture recognition module is used for setting a corresponding threshold value, comparing the threshold value with the inter-frame similarity and judging to obtain a static picture; and the record output module is used for recording static picture information corresponding to the static picture and outputting the static picture information to the display equipment. According to the method, the dynamic and static images in the elevator monitoring video can be stored at the same time, the integrity of the monitoring images is guaranteed, the accuracy of video monitoring is improved, and different from an existing AI algorithm, the SSIM algorithm used in the method is small in calculation amount and low in cost.

Description

Technical Field

[0001] The present invention relates to the technical field of digital image processing and computer vision, and in particular to an elevator monitoring video still picture recognition system and method based on a SSIM algorithm. Background Art

[0002] As an indispensable means of transportation in modern buildings, the safety of elevators has always been a concern. Elevator monitoring video uses video acquisition technology to install high-definition cameras inside the elevator car and key operating areas. By recording videos in real time and transmitting video signals during the elevator operation, comprehensive monitoring and recording of the elevator operation status can be achieved. Elevator monitoring video uses high-definition cameras to monitor the interior of the elevator car in high definition, and transmits the elevator operation status data to the monitoring center through network communication technology to achieve remote real-time monitoring of the elevator. At the same time, with the help of algorithms, real-time scene analysis of the video stream is performed to identify special scenes and behaviors, such as trapped events, abnormal behaviors, etc., and make corresponding feedback decisions. In addition, in order to ensure that important event videos can be preserved for a long time, non-important videos can be intelligently filtered to save storage space. Therefore, elevator monitoring video is an important part of the safe operation and management of elevators, providing a strong guarantee for the safe operation of elevators. The SSIM algorithm is an image quality assessment method that simulates the characteristics of the human visual system. It can comprehensively consider the similarities in brightness, contrast and structure, and more accurately reflect the similarity between video images. The SSIM algorithm can then be applied to the recognition of still images in elevator monitoring videos.

[0003] In the prior art, traditional elevator monitoring systems mostly rely on manual monitoring, but this method often has low monitoring efficiency, is prone to fatigue, and has a high false alarm rate. In addition, in order to save storage space and traffic, existing elevator monitoring systems often only save dynamic images, but cannot save static images, and cannot guarantee the integrity of the monitoring images and the integrity of the monitoring video, resulting in poor accuracy of video monitoring. However, although existing AI algorithms can also save static images of monitoring videos and detect whether there are people or objects in the elevator in real time, this method often has a large amount of calculations and high costs.

[0004] Therefore, how to solve the problem that the traditional elevator monitoring system cannot save static images, cannot ensure the integrity of the monitoring images and the integrity of the monitoring video, the accuracy of video monitoring is poor, and the existing algorithms have large computational complexity and high cost, has become a problem that needs to be solved urgently. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a still picture recognition system and method for elevator monitoring video based on SSIM algorithm, which collects monitoring video data in the elevator and transmits the monitoring video data to a preprocessing module; preprocesses the monitoring video data to obtain preprocessed monitoring video data, that is, target video data; processes the target video data through SSIM algorithm to obtain the inter-frame similarity of the target video data; sets a corresponding threshold, compares the threshold with the inter-frame similarity, and determines whether a still picture is obtained; records the still picture information corresponding to the still picture, and outputs the still picture information to a display device, so that not only the dynamic picture in the elevator monitoring video can be saved, but also the static picture can be saved, thereby ensuring the integrity of the elevator monitoring picture and the integrity of the monitoring video, and improving the accuracy of elevator video monitoring. At the same time, unlike the existing AI algorithm, the SSIM algorithm used in the present invention has a small amount of calculation and a low cost, and is more suitable for actual elevator monitoring video systems.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0007] The present invention discloses an elevator monitoring video still picture recognition system based on the SSIM algorithm, comprising:

[0008] A video acquisition module is used to collect monitoring video data in the elevator and transmit the monitoring video data to the preprocessing module;

[0009] A preprocessing module, used to preprocess the monitoring video data to obtain preprocessed monitoring video data, i.e., target video data;

[0010] A video processing module, used to process the target video data by using the SSIM algorithm to obtain the inter-frame similarity of the target video data;

[0011] A still picture recognition module, used for setting a corresponding threshold, comparing the threshold with the inter-frame similarity, and determining whether a still picture is obtained;

[0012] The recording and outputting module is used to record the still picture information corresponding to the still picture and output the still picture information to a display device.

[0013] A further improvement of the present invention is that the video acquisition module comprises:

[0014] An acquisition unit, used to collect monitoring video data inside the elevator in real time and acquire the monitoring video data;

[0015] A transmission unit is used to transmit the monitoring video data to the pre-processing module.

[0016] A further improvement of the present invention is that the preprocessing module is used to preprocess the monitoring video data using the following video preprocessing algorithm to obtain preprocessed monitoring video data, i.e., target video data:

[0017] Convert the target video data from the RGB channel to the HSV channel to obtain H channel data, S channel data and V channel data respectively;

[0018] The V channel data is enhanced twice, including:

[0019] The V channel data is subjected to a first enhancement process to obtain first enhanced channel data, wherein the expression of the first enhanced channel data is:

[0020]

[0021] Where V 1 (x, y) represents the pixel value of the first enhanced channel data at the pixel point (x, y); v(x, y) represents the pixel value of the V channel data at the pixel point (x, y); max represents the maximum value;

[0022] The first enhanced channel data is subjected to a second enhancement process to obtain second enhanced channel data, wherein the expression of the second enhanced channel data is:

[0023] V 2 (x,y)=(0.013Y+0.015)[V 1 (x,y)] 3 +(1.093Y+0.836)[V 1 (x,y)] 2 -(3.079Y

[0024] -1.734)V 1 (x,y)+(2.978Y-2.606)

[0025] Where V 2 (x, y) represents the pixel value of the second enhanced channel data at the pixel point (x, y); V 1 (x, y) represents the pixel value of the first enhanced channel data at the pixel point (x, y); Y represents the enhanced distinction threshold;

[0026] The second enhanced channel data is the V channel data after two enhancement processes. The V channel data after two enhancement processes are merged with the H channel data and the S channel data to obtain the pre-processed monitoring video data, that is, the target video data.

[0027] A further improvement of the present invention is that: the video processing module is used to: process the target video data by the SSIM algorithm using the following SSIM similarity algorithm to obtain the inter-frame similarity of the target video data:

[0028] Selecting a local window and sliding the local window on the video frame of the target video data;

[0029] The brightness mean and contrast variance of the pixels in each local window, as well as the structural covariance with the pixels in other local windows, can be calculated to obtain the statistical characteristics of the local window;

[0030] According to the statistical features, the SSIM values ​​of the adjacent video frames of the target video data are calculated, and the expression of the SSIM value is:

[0031]

[0032] Where m and n represent adjacent video frames in the target video data; μ m , μ n Respectively represent the brightness mean of pixels in adjacent video frames m and n in the local window; σ m 2 , σ n 2 Respectively represent the contrast variance of pixels in adjacent video frames m and n in the local window; σ mn represents the structural covariance of pixels of adjacent video frames m and n in the local window; a 1 、a 2 represents a constant, and a 1 ≠0, a 2 ≠0, to avoid the situation where the denominator is zero;

[0033] After obtaining the SSIM values ​​of the adjacent video frames of the target video data, weighted averaging the SSIM values ​​of all windows in the video frame is performed to obtain a global SSIM value, i.e., the inter-frame similarity of the target video data, wherein the value range of the inter-frame similarity is between 0 and 1, and the larger the value of the inter-frame similarity is, the higher the similarity of the adjacent video frames of the target video data is;

[0034] The brightness, contrast and structure similarities of adjacent video frames of the target video data are compared by using the SSIM algorithm to obtain the inter-frame similarity of the target video data.

[0035] A further improvement of the present invention is that the still picture recognition module comprises:

[0036] A setting unit, used to set a corresponding threshold value according to the monitoring requirements of the elevator;

[0037] A receiving unit, configured to receive the inter-frame similarity of the target video data;

[0038] A comparison and identification unit is used to compare the threshold with the inter-frame similarity, count the number of continuous video frames whose inter-frame similarity exceeds the threshold, and if the number of continuous video frames meets a preset number condition, determine that the continuous video frames correspond to a still picture.

[0039] A further improvement of the present invention is that: the preset number condition is that the number of the continuous video frames is greater than or equal to a preset still number.

[0040] A further improvement of the present invention is that the record output module comprises:

[0041] A marking unit, used for marking the continuous video frames to obtain marked video frames;

[0042] A recording unit, used to record still picture information corresponding to the marked video frame;

[0043] An output unit is used to output the still picture information to the display device.

[0044] The present invention provides a method for identifying still images of elevator monitoring videos based on the SSIM algorithm, comprising:

[0045] Collecting surveillance video data in the elevator and transmitting the surveillance video data to the preprocessing module;

[0046] Preprocessing the monitoring video data to obtain preprocessed monitoring video data, i.e., target video data;

[0047] The target video data is processed by using the SSIM algorithm to obtain the inter-frame similarity of the target video data;

[0048] Setting a corresponding threshold, comparing the threshold with the inter-frame similarity, and determining whether a still picture is obtained;

[0049] The still picture information corresponding to the still picture is recorded, and the still picture information is output to a display device.

[0050] The beneficial effects of the present invention are as follows: the present invention proposes a still picture recognition system and method for elevator monitoring video based on the SSIM algorithm, which collects monitoring video data in the elevator and transmits the monitoring video data to a preprocessing module; preprocesses the monitoring video data to obtain preprocessed monitoring video data, namely, target video data; processes the target video data through the SSIM algorithm to obtain the inter-frame similarity of the target video data; sets a corresponding threshold, compares the threshold with the inter-frame similarity, and determines to obtain a still picture; records still picture information corresponding to the still picture, and outputs the still picture information to a display device, thereby not only saving dynamic pictures in the elevator monitoring video, but also saving static pictures, thereby ensuring the integrity of the elevator monitoring picture and the integrity of the monitoring video, and improving the accuracy of elevator video monitoring. At the same time, unlike the existing AI algorithm, the SSIM algorithm used in the present invention has a small amount of calculation and a low cost, and is more suitable for actual elevator monitoring video systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A structural diagram of a still image recognition system for elevator monitoring video based on the SSIM algorithm provided by an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of the structure of a video acquisition module of an elevator monitoring video still picture recognition system based on the SSIM algorithm provided by an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the structure of a still picture recognition module of an elevator monitoring video still picture recognition system based on the SSIM algorithm provided by an embodiment of the present invention;

[0054] Figure 4 A structural schematic diagram of a recording output module of an elevator monitoring video still picture recognition system based on the SSIM algorithm provided in an embodiment of the present invention;

[0055] Figure 5 A schematic flow chart of a method for still picture recognition of elevator monitoring video based on the SSIM algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0057] See also Figure 1, a still picture recognition system for elevator monitoring video based on the SSIM algorithm of this embodiment includes:

[0058] The video acquisition module is used to collect the monitoring video data in the elevator and transmit the monitoring video data to the preprocessing module;

[0059] A preprocessing module is used to preprocess the monitoring video data to obtain the preprocessed monitoring video data, that is, the target video data;

[0060] The video processing module is used to process the target video data by using the SSIM algorithm to obtain the inter-frame similarity of the target video data;

[0061] A still picture recognition module is used to set a corresponding threshold, compare the threshold with the inter-frame similarity, and determine whether a still picture is obtained;

[0062] The recording and outputting module is used to record the still picture information corresponding to the still picture and output the still picture information to the display device.

[0063] In practical applications, SSIM (Structural Similarity Index Measure) is an indicator used to measure the similarity between two images. The algorithm evaluates the similarity between images by comparing the three key features of image brightness, contrast and structure. The SSIM algorithm is based on three key features of images: Luminance: measured by average grayscale, obtained by calculating the average value of all pixel values ​​in the image; Contrast: measured by grayscale standard deviation, reflecting the drastic degree of image brightness and darkness changes; Structure: compares the correlation of pixel values ​​in the image after normalization, that is, the structural information of the image. The SSIM algorithm calculates the similarity of the two images in brightness, contrast and structure respectively, and weights and multiplies these three similarity values ​​to obtain the final SSIM value. The SSIM value ranges from -1 to 1, and the larger the value, the more similar the two images are.

[0064] Among them, the SSIM algorithm has a wide range of applications in the field of image processing, including: image quality assessment: used to evaluate the quality loss after image compression, transmission or restoration; image denoising: as a performance evaluation index of the denoising algorithm; image enhancement: in the image enhancement process, the enhancement effect is evaluated by comparing the images before and after enhancement; image reconstruction: in the image reconstruction task, it is used as a loss function to guide the reconstruction process. The SSIM algorithm is an effective image similarity measurement method, which evaluates the similarity between images by comparing the three key features of image brightness, contrast and structure. The algorithm has broad application prospects and important research value in the field of image processing. Therefore, the present invention applies the SSIM algorithm to the elevator monitoring video system, which can not only save the dynamic pictures in the elevator monitoring video, but also save the static pictures, ensuring the integrity of the elevator monitoring picture and the integrity of the monitoring video, and improving the accuracy of the elevator video monitoring. At the same time, unlike the existing AI algorithm, the SSIM algorithm used in the present invention has a small amount of calculation and low cost, and is more suitable for actual elevator monitoring video systems.

[0065] Among them, see Figure 2 , video acquisition module, including:

[0066] An acquisition unit, used to collect monitoring video data inside the elevator in real time and acquire the monitoring video data;

[0067] The transmission unit is used to transmit the monitoring video data to the pre-processing module.

[0068] It is understandable that the video acquisition module can input the collected surveillance video data inside the elevator into the subsequent processing module in frames, so as to facilitate the subsequent corresponding processing of the video frames in the surveillance video data through the SSIM algorithm to obtain the inter-frame similarity of the surveillance video data.

[0069] Through the above steps, the processing efficiency and accuracy of elevator monitoring can be improved, the situation inside the elevator can be monitored in real time, and the automation level of elevator monitoring video can be improved.

[0070] The preprocessing module is used to preprocess the surveillance video data using the following video preprocessing algorithm to obtain the preprocessed surveillance video data, i.e., the target video data:

[0071] Convert the target video data from RGB channel to HSV channel to obtain H channel data, S channel data and V channel data respectively;

[0072] The V channel data is enhanced twice, including:

[0073] The V channel data is subjected to a first enhancement process to obtain first enhanced channel data. The expression of the first enhanced channel data is:

[0074]

[0075] Where V 1 (x, y) represents the pixel value of the first enhanced channel data at the pixel point (x, y); v(x, y) represents the pixel value of the V channel data at the pixel point (x, y); max represents the maximum value;

[0076] The first enhanced channel data is enhanced for the second time to obtain the second enhanced channel data. The expression of the second enhanced channel data is:

[0077] V 2 (x,y)=(0.013Y+0.015)[V 1 (x,y)] 3 +(1.093Y+0.836)[V 1 (x,y)] 2 -(3.079Y

[0078] -1.734)V 1 (x,y)+(2.978Y-2.606)

[0079] Where V 2 (x, y) represents the pixel value of the second enhanced channel data at the pixel point (x, y); V 1 (x, y) represents the pixel value of the first enhanced channel data at the pixel point (x, y); Y represents the enhanced distinction threshold;

[0080] The second enhanced channel data is the V channel data after two enhancement processes. The V channel data after two enhancement processes are merged with the H channel data and the S channel data to obtain the pre-processed monitoring video data, that is, the target video data.

[0081] In practical applications, by performing corresponding preprocessing operations on the obtained surveillance video data, the quality of the surveillance video data can be significantly improved, thereby improving the accuracy and efficiency of subsequent processing. At the same time, the detailed information in the surveillance video can be maintained or restored, making the surveillance video clearer, improving the visual quality of the surveillance video, and adjusting the color saturation, brightness and other parameters of the surveillance video to make the color of the surveillance video more vivid and lively.

[0082] The video processing module is used to process the target video data using the following SSIM similarity algorithm to obtain the inter-frame similarity of the target video data:

[0083] Select a local window and slide the local window on the video frame of the target video data;

[0084] By calculating the brightness mean and contrast variance of the pixels in each local window, as well as the structural covariance with the pixels in other local windows, the statistical characteristics of the local window can be obtained;

[0085] According to the statistical characteristics, the SSIM values ​​of adjacent video frames of the target video data are calculated. The expression of the SSIM value is:

[0086]

[0087] Where m and n represent adjacent video frames in the target video data; μ m , μ n Respectively represent the brightness mean of pixels in adjacent video frames m and n in the local window; σ m 2 , σ n 2 Respectively represent the contrast variance of pixels in adjacent video frames m and n in the local window; σ mn represents the structural covariance of pixels of adjacent video frames m and n in the local window; a 1 、a 2 represents a constant, and a 1 ≠0, a 2 ≠0, to avoid the situation where the denominator is zero;

[0088] After obtaining the SSIM values ​​of the adjacent video frames of the target video data, the SSIM values ​​of all windows in the video frame are weighted averaged to obtain the global SSIM value, that is, the inter-frame similarity of the target video data. The value range of the inter-frame similarity is between 0 and 1. The larger the value of the inter-frame similarity is, the higher the similarity of the adjacent video frames of the target video data is.

[0089] The SSIM algorithm is used to compare the similarity of brightness, contrast and structure of adjacent video frames of the target video data to obtain the inter-frame similarity of the target video data.

[0090] Through the above steps, the accuracy of elevator video monitoring can be improved. Different from the existing AI algorithm, the SSIM algorithm used in the present invention has a small amount of calculation and a low cost, and is more suitable for actual elevator monitoring video systems.

[0091] Among them, see Figure 3 , a still picture recognition module, comprising:

[0092] A setting unit, used to set corresponding thresholds according to the monitoring requirements of the elevator;

[0093] A receiving unit, configured to receive inter-frame similarity of target video data;

[0094] The comparison and identification unit is used to compare the threshold with the inter-frame similarity, count the number of continuous video frames whose inter-frame similarity exceeds the threshold, and if the number of continuous video frames meets a preset number condition, determine that the continuous video frames correspond to a still picture.

[0095] It should be noted that the preset number condition is that the number of continuous video frames is greater than or equal to the preset still number.

[0096] It is understandable that when setting the corresponding threshold, factors such as the clarity, frame rate and definition of still images of the actual elevator monitoring video need to be considered to meet the relevant monitoring needs of the actual elevator. After determining the required threshold, the inter-frame similarity obtained by the video processing module can be compared with the threshold. The preset still number refers to the number of continuous video frames for judging still images preset in advance by the staff. When there are multiple consecutive frames with an inter-frame similarity greater than the threshold, and the number of continuous video frames is greater than the preset still number, it can be judged that these continuous video frames correspond to still images.

[0097] Through the above steps, not only the dynamic images in the elevator monitoring video can be saved, but also the static images can be saved, thereby ensuring the integrity of the elevator monitoring images and the integrity of the monitoring video, and improving the accuracy of elevator video monitoring. At the same time, unlike the existing AI algorithm, the SSIM algorithm used in the present invention has a small amount of calculation and a low cost, and is more suitable for actual elevator monitoring video systems.

[0098] Among them, see Figure 4 , record output module, including:

[0099] A marking unit, used for marking continuous video frames to obtain marked video frames;

[0100] A recording unit, used for recording still picture information corresponding to the marked video frame;

[0101] The output unit is used to output the still picture information to a display device.

[0102] In practical applications, continuous video frames determined in the monitoring video can be automatically marked to obtain corresponding marked video frames. These marked video frames correspond to still images, and the corresponding still image information can be recorded, such as still time, position, etc. After obtaining the still image information, the still image information can be output to the display device corresponding to the elevator monitoring, so that the management personnel can view and process it in time, and take corresponding measures, thereby improving the real-time performance of elevator monitoring and facilitating the staff to feedback and process the corresponding situations in time.

[0103] See also Figure 5 The present invention provides a method for identifying still images of elevator monitoring videos based on the SSIM algorithm, comprising:

[0104] Collect the surveillance video data in the elevator and transmit the surveillance video data to the preprocessing module;

[0105] Preprocessing the surveillance video data to obtain preprocessed surveillance video data, i.e., target video data;

[0106] The target video data is processed by the SSIM algorithm to obtain the inter-frame similarity of the target video data;

[0107] Set a corresponding threshold, compare the threshold with the inter-frame similarity, and determine whether a still picture is obtained;

[0108] The still picture information corresponding to the still picture is recorded, and the still picture information is output to a display device.

[0109] Through the introduction of the above embodiments, the present invention can collect monitoring video data in the elevator through a system and method for still picture recognition of elevator monitoring video based on the SSIM algorithm, and transmit the monitoring video data to a preprocessing module; preprocess the monitoring video data to obtain preprocessed monitoring video data, that is, target video data; process the target video data through the SSIM algorithm to obtain the inter-frame similarity of the target video data; set a corresponding threshold, compare the threshold with the inter-frame similarity, and determine whether a still picture is obtained; record the still picture information corresponding to the still picture, and output the still picture information to a display device, so that not only the dynamic picture in the elevator monitoring video can be saved, but also the static picture can be saved, thereby ensuring the integrity of the elevator monitoring picture and the integrity of the monitoring video, and improving the accuracy of elevator video monitoring. At the same time, unlike the existing AI algorithm, the SSIM algorithm used in the present invention has a small amount of calculation and a low cost, and is more suitable for actual elevator monitoring video systems.

[0110] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have the same meaning as in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined similarly here.

[0111] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An elevator monitoring video still picture recognition system based on the SSIM algorithm, characterized by: include: A video acquisition module is used to collect monitoring video data in the elevator and transmit the monitoring video data to the preprocessing module; A preprocessing module, used to preprocess the monitoring video data to obtain preprocessed monitoring video data, i.e., target video data; A video processing module, used to process the target video data by using the SSIM algorithm to obtain the inter-frame similarity of the target video data; A still picture recognition module, used for setting a corresponding threshold, comparing the threshold with the inter-frame similarity, and determining whether a still picture is obtained; The recording and outputting module is used to record the still picture information corresponding to the still picture and output the still picture information to a display device.

2. According to claim 1, a still picture recognition system for elevator monitoring video based on SSIM algorithm is characterized in that: The video acquisition module comprises: An acquisition unit, used to collect monitoring video data inside the elevator in real time and acquire the monitoring video data; A transmission unit is used to transmit the monitoring video data to the pre-processing module.

3. According to claim 1, a still picture recognition system for elevator monitoring video based on SSIM algorithm is characterized in that: The preprocessing module is used to preprocess the monitoring video data using the following video preprocessing algorithm to obtain preprocessed monitoring video data, i.e., target video data: Convert the target video data from the RGB channel to the HSV channel to obtain H channel data, S channel data and V channel data respectively; The V channel data is enhanced twice, including: The V channel data is subjected to a first enhancement process to obtain first enhanced channel data, wherein the expression of the first enhanced channel data is: Wherein, V1(x, y) represents the pixel value of the first enhanced channel data at the pixel point (x, y); v(x, y) represents the pixel value of the V channel data at the pixel point (x, y); max represents the maximum value; The first enhanced channel data is subjected to a second enhancement process to obtain second enhanced channel data, wherein the expression of the second enhanced channel data is: V2(x,y)=(0.013Y+0.015)[V1(x,y)] 3 +(1.093Y+0.836)[V1(x,y)] 2 -(3.079Y -1.734)V1(x,y)+(2.978Y-2.606) Wherein, V2(x, y) represents the pixel value of the second enhanced channel data at the pixel point (x, y); V1(x, y) represents the pixel value of the first enhanced channel data at the pixel point (x, y); Y represents the enhancement discrimination threshold; The second enhanced channel data is the V channel data after two enhancement processes. The V channel data after two enhancement processes are merged with the H channel data and the S channel data to obtain the pre-processed monitoring video data, that is, the target video data.

4. The elevator monitoring video still picture recognition system based on the SSIM algorithm according to claim 1 is characterized in that: The video processing module is used to process the target video data by using the following SSIM similarity algorithm to obtain the inter-frame similarity of the target video data: Selecting a local window and sliding the local window on the video frame of the target video data; The brightness mean and contrast variance of the pixels in each local window, as well as the structural covariance with the pixels in other local windows, can be calculated to obtain the statistical characteristics of the local window; According to the statistical features, the SSIM values ​​of the adjacent video frames of the target video data are calculated, and the expression of the SSIM value is: Where m and n represent adjacent video frames in the target video data; μ m , μ n Respectively represent the brightness mean of pixels in adjacent video frames m and n in the local window; σ m 2 , σ n 2 Respectively represent the contrast variance of pixels in adjacent video frames m and n in the local window; σ mn Represents the structural covariance of pixels of adjacent video frames m and n in the local window; a1 and a2 represent constants, and a1≠0, a2≠0, to avoid the situation where the denominator is zero; After obtaining the SSIM values ​​of the adjacent video frames of the target video data, weighted averaging the SSIM values ​​of all windows in the video frame is performed to obtain a global SSIM value, i.e., the inter-frame similarity of the target video data, wherein the value range of the inter-frame similarity is between 0 and 1, and the larger the value of the inter-frame similarity is, the higher the similarity of the adjacent video frames of the target video data is; The brightness, contrast and structure similarities of adjacent video frames of the target video data are compared by using the SSIM algorithm to obtain the inter-frame similarity of the target video data.

5. The elevator monitoring video still picture recognition system based on the SSIM algorithm according to claim 1 is characterized in that: The still picture recognition module comprises: A setting unit, used to set a corresponding threshold value according to the monitoring requirements of the elevator; A receiving unit, configured to receive the inter-frame similarity of the target video data; A comparison and identification unit is used to compare the threshold with the inter-frame similarity, count the number of continuous video frames whose inter-frame similarity exceeds the threshold, and if the number of continuous video frames meets a preset number condition, determine that the continuous video frames correspond to a still picture.

6. The elevator monitoring video still picture recognition system based on the SSIM algorithm according to claim 5 is characterized in that: The preset number condition is that the number of the continuous video frames is greater than or equal to a preset still number.

7. The elevator monitoring video still picture recognition system based on the SSIM algorithm according to claim 1 is characterized in that: The record output module comprises: A marking unit, used for marking the continuous video frames to obtain marked video frames; A recording unit, used to record still picture information corresponding to the marked video frame; An output unit is used to output the still picture information to the display device.

8. The recognition method of the elevator monitoring video still picture recognition system based on the SSIM algorithm according to claim 1 is characterized in that: include: Collecting surveillance video data in the elevator and transmitting the surveillance video data to the preprocessing module; Preprocessing the monitoring video data to obtain preprocessed monitoring video data, i.e., target video data; The target video data is processed by using the SSIM algorithm to obtain the inter-frame similarity of the target video data; A corresponding threshold is set, and the threshold is compared with the inter-frame similarity to determine whether a still picture is obtained; still picture information corresponding to the still picture is recorded, and the still picture information is output to a display device.

Images