Attribute analysis system and estimation method and communication method using the same

Abstract

To solve the problem that a monitoring camera needs to be installed in a place where an environment such as a power supply or a communication line is not provided, and it may be difficult to stably transmit video data which needs to be analyzed to an analysis server.SOLUTION: A monitoring camera 10 including a power supply part including a battery and a solar panel, a recording medium that stores a captured moving image and an image captured in a first cycle, and a communication part that transmits the stored moving image or the image, and an artificial intelligence server 20 that analyzes the moving image or the image received from the recording medium to detect a person and analyzes the number and attributes of the person are provided. The artificial intelligence server 20 obtains a correction value between the number of persons detected from the moving image and the number of persons detected from the image by a statistical method, and applies the correction value to the number of persons detected from the image photographed in a first cycle to obtain the number of persons at the time of photographing the image, for the moving image and the image photographed at the same time a plurality of times in a predetermined period in advance.SELECTED DRAWING: Figure 1A

Description

[Technical Field]

[0001] The present invention relates to an attribute analysis system and an attribute analysis method using a surveillance camera, and in particular to an attribute analysis camera system and attribute analysis method therefor that can be installed regardless of the facility environment such as a power source or optical fiber line and that reduces power consumption and communication volume. [Background technology]

[0002] Demand for systems that use surveillance cameras and AI (artificial intelligence) to analyze the number and attributes of subjects is increasing year by year. Such attribute analysis systems are used not only for crime prevention and deterrence and disaster prevention monitoring, but also for marketing and traffic volume surveys. Therefore, attribute analysis systems are required to be installed in various locations depending on their purpose.

[0003] As examples of attribute analysis systems, for example, Patent Documents 1 and 2 describe a human attribute estimation system equipped with a surveillance camera. Here, Patent Document 1 describes that in order to improve the accuracy of estimating the attributes of a person who is the subject of attribute estimation, regardless of the installation environment of the surveillance camera, estimation parameters based on learning data having environment-dependent attributes similar to the environment-dependent attributes of the surveillance camera are used. Furthermore, Patent Document 2 addresses the reduction in attribute estimation accuracy caused by the illuminance of a person photographed by a surveillance camera being different from the illuminance of a sample image. Patent Document 2 describes how a pseudo-on-site image is generated based on photographing environment data indicating the photographing environment under which the person whose attributes are to be estimated is photographed and a standard image that is a person image, and how an estimation model is trained using this pseudo-on-site image.

[0004] However, both Patent Document 1 and Patent Document 2 are premised on the use of surveillance cameras in stores. For this reason, neither Patent Document 1 nor Patent Document 2 describes, for example, stable communication of data from a surveillance camera when the surveillance camera is installed outdoors or in an open space where it is difficult to secure a power source, or a case where it is desired to analyze and output in real time the degree of congestion over a certain time period (such as one hour), or, in the case of a system that analyzes only images specialized for more power-saving and low-volume data, outputting with a small error the number of people detected in video over a certain time period (such as one hour) from the number of people detected in snapshot images (still images) taken by the surveillance camera. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-252507 [Patent Document 2] Japanese Patent Application Laid-Open No. 2013-242825 Summary of the Invention [Problem to be solved by the invention]

[0006] Depending on the purpose, surveillance cameras installed in locations to be monitored may need to be installed in places where the environment, such as power supply and communication lines, is not well-established. However, surveillance cameras usually require a power source to capture and transmit images. Therefore, the locations where surveillance cameras can be installed are limited to places where a power supply can be supplied. Furthermore, for example, if an environment such as an optical fiber line is not available as a communication line, the attribute analysis system must communicate with the surveillance camera using a wireless line (LTE line). However, when communicating using such an LTE line, it may be difficult to stably transmit data from the surveillance camera to the analysis server due to the large data size of the video captured by the surveillance camera. In such cases, it may not be possible to perform high-precision analysis using artificial intelligence (AI). Furthermore, when transmitting video, the large data size can lead to communication errors, which means that the AI ​​(artificial intelligence) server cannot receive the video data necessary for analysis, making it impossible for the AI ​​server to perform attribute analysis. In addition, analyzing a video takes a certain amount of time due to the data size, making it difficult to analyze attributes in a short time. [Means for solving the problem]

[0007] The present invention provides an attribute analysis system that solves the above-mentioned problems. Specifically, the present invention provides: a surveillance camera including a power supply unit including a battery and a solar panel for charging the battery, a recording medium for storing captured videos and images captured at a first interval, and a communication unit for transmitting the videos or images recorded on the recording medium; an artificial intelligence server including a video storage unit that records the video received from the recording medium, an image storage unit that records the images received from the recording medium, and a central processing unit that analyzes the video or the images to detect people and analyzes the number of people and their attributes (gender, age); It is equipped with The artificial intelligence server controls the central processing unit to: For the video and the images taken multiple times at the same time during a predetermined period, the number of people detected from the video recorded in the video storage unit and the number of people detected from the images recorded in the image storage unit are calculated, and an approximate calculation is performed using a statistical method between the number of people detected from the video and the number of people detected from the images to calculate a correction value between the video and the images; The correction value is applied to the number of people detected in each of the images captured in the first cycle, and the number of people in the video for a certain time period at the time the images were captured is calculated, and a real-time people count for the certain time period is simulated and analyzed. The present invention provides an attribute analysis system.

[0008] The present invention also provides a surveillance camera equipped with a power supply unit including a battery and a solar panel for charging the battery, a recording medium for storing captured video, and a communication unit for transmitting the video recorded on the recording medium; an artificial intelligence server including a video storage unit that records the video received from the recording medium, and a central processing unit that analyzes the video recorded in the video storage unit to detect people and calculate the number of people; An attribute analysis system comprising: The artificial intelligence server (a) causing the surveillance camera to transmit video images captured over a predetermined period of time to the video storage unit, and monitoring for occurrence of a communication error between the surveillance camera and the artificial intelligence server; (b) when the video is transmitted to the video storage unit without detecting the communication error, the central processing unit is caused to analyze the video, detect people, and calculate the number of people; (c) when the communication error is detected, causing the surveillance camera to retransmit the video to the video storage unit, and monitoring the occurrence of the communication error between the surveillance camera and the artificial intelligence server during a first period; (d) repeating the steps (a) to (c) above for a second period longer than the first period; An attribute analysis system is also provided.

[0009] Here, it is preferable that the communication unit is equipped with a SIM card, or that the communication unit further includes an operation console that communicates with the artificial intelligence server via a wired or wireless connection using an Internet line.

[0010] Furthermore, the present invention also provides an estimation method in an attribute analysis system. in particular, a surveillance camera including a power supply unit including a battery and a solar panel for charging the battery, a recording medium for storing captured videos and images captured at a first interval, and a communication unit for transmitting the videos or images recorded on the recording medium; an artificial intelligence server including a video storage unit that records the video received from the recording medium, an image storage unit that records the images received from the recording medium, and a central processing unit that analyzes the video or the images to detect people and determine the number of people; An estimation method in an attribute analysis system comprising: The artificial intelligence server controls the central processing unit to: a step of calculating the number of people detected from the video recorded in the video storage unit and the number of people detected from the images recorded in the image storage unit for the video and the images taken multiple times at the same time over a predetermined period in advance, and performing an approximate calculation using a statistical method between the number of people detected from the video and the number of people detected from the images to calculate a correction value between the video and the images; applying the correction value to the number of people detected in each of the images captured in the first period, to determine the number of people at the time the image was captured; The present invention provides an estimation method in an attribute analysis system, comprising:

[0011] Additionally, the present invention provides a communication method in an attribute analysis system. in particular, a surveillance camera including a power supply unit having a battery and a solar panel for charging the battery, a recording medium for storing captured video, and a communication unit for transmitting the video recorded on the recording medium; an artificial intelligence server including a video storage unit that records the video received from the recording medium, and a central processing unit that analyzes the video recorded in the video storage unit to detect people and analyzes the number of people and their attributes (gender, age), A communication method in an attribute analysis system comprising: The artificial intelligence server (a') causing the surveillance camera to transmit video images captured over a predetermined period of time to the video storage unit, and monitoring for occurrence of a communication error between the surveillance camera and the artificial intelligence server; (b') when the video is transmitted to the video storage unit without detecting the communication error, causing the central processing unit to analyze the video, detect people, and calculate the number of people; (c') when the communication error is detected, causing the surveillance camera to retransmit the video to the video storage unit, and monitoring the occurrence of the communication error between the surveillance camera and the artificial intelligence server during a first period; (d') repeating the steps (a') to (c') above during a second period longer than the first period; The present invention provides a communication method in an attribute analysis system that executes the above. [Effects of the Invention]

[0012] The surveillance camera according to the present invention is equipped with a charging module including a solar panel and a battery. This allows the surveillance camera to be installed in places where there is no power supply. The surveillance camera also has a communication unit with a built-in SIM (Subscriber Identity Module) card, for example. This means that other devices installed together with the surveillance camera are not required, allowing the surveillance camera to be installed in a more space-saving location. The AI ​​server of the present invention employs a video acquisition algorithm that monitors communication errors between the surveillance camera and the AI ​​server during video transmission during video analysis, and requests the surveillance camera to resend data if a communication error occurs. This improves the reliability of video acquisition between the surveillance camera and the AI ​​server, enabling stable analysis by the AI ​​server. Furthermore, the AI ​​server according to the present invention can perform not only video analysis but also snapshot analysis, which analyzes images (whose data size is smaller than that of videos). Therefore, snapshot analysis can reduce the time required for analysis compared to video analysis, thereby achieving analysis with less delay. Furthermore, the AI ​​server of the present invention performs statistical approximation calculations on the number of people determined from videos and images acquired at the same time over the same period to obtain a correction value between the two, and then applies the obtained correction value to the number of people determined by snapshot analysis. Therefore, the AI ​​server of the present invention can obtain accuracy in the number of people from snapshot analysis that is close to that of video analysis. Approximation calculation methods using statistical processing include methods that use machine learning and methods that use statistical methods that include mathematical methods, such as linear regression and state space models. [Brief explanation of the drawings]

[0013] [Figure 1] 1A is a block diagram showing the configuration of the attribute analysis system 1 of the present invention. 1B is a photograph showing a person detected by the artificial intelligence server 20 being marked. [Figure 2] FIG. 10 is a data flow diagram showing the processing performed in the attribute analysis system 1 when a communication error occurs during communication of a video file executed during video analysis. [Figure 3] FIG. 2 is a data flow diagram showing processing during snapshot analysis in the attribute analysis system 1. [Figure 4] FIG. 2 is a flowchart showing the processing performed when analyzing a moving image in the attribute analysis system 1. [Figure 5] FIG. 2 is a flowchart showing processing during snapshot analysis in the attribute analysis system 1. [Figure 6] This is measurement data obtained by analyzing the video files and image files acquired by the attribute analysis system 1 over a two-month period and tallying up the number of people who analyzed the video and the number of people who analyzed the snapshots. [Figure 7] 7 is a graph for determining an optimal correction coefficient between the number of people analyzed by video analysis and the number of people analyzed by snapshot analysis by performing regression analysis on the measurement data of FIG. 6 as an example of approximate calculation of statistical processing. [Figure 8] 8 is a graph of basic statistics obtained from the regression analysis shown in FIG. 7. [Figure 9] 8 is a graph showing the error (before correction) between the number of people analyzed by video and the number of people analyzed by snapshot, and the error (after correction) when the correction value obtained from the regression analysis of FIG. 7 is used. DETAILED DESCRIPTION OF THE INVENTION

[0014] Referring to FIG. 1A, an attribute analysis system 1 according to one embodiment of the present invention will be described. The attribute analysis system 1 includes a surveillance camera 10, an artificial intelligence server 20 in communication with the surveillance camera 10, and a console PC 30 in communication with the artificial intelligence server 20. The surveillance camera 10 and the artificial intelligence server 20, and the artificial intelligence server 20 and the console PC 30 may be connected, for example, by a wired connection using a communication cable, wirelessly, or by both a wired and wireless connection. In particular, when the surveillance camera 10 is installed outdoors, it is preferable that the surveillance camera 10 and the artificial intelligence server 20 be connected at least wirelessly via an internet line.

[0015] The surveillance camera 10 comprises a power supply unit 101, a recording medium 102 that records video (images) captured by the surveillance camera or images (still images) captured at a predetermined interval (e.g., a one-minute interval as a first interval), and a communication unit 103 that communicates with the artificial intelligence server 20. The power supply unit 101 comprises a battery 101a and a solar panel 101b that charges the battery 101a. By including such a solar panel 101b, the surveillance camera 10 can be installed without being restricted by power sources, for example, outdoors where no power supply facilities are available. The recording medium 102 may be any of a hard disk drive, SSD, or memory card with sufficient capacity to record the captured video or images. The communication unit 103 may, for example, incorporate a SIM card.

[0016] The artificial intelligence server 20 comprises a recording unit 201 that records videos or images transmitted from the surveillance camera 10, and a central processing unit 202 that communicates with the recording unit 201 and performs calculations such as analyzing the videos or images. The recording unit 201 comprises a video storage unit 201a that records videos and analysis programs from the surveillance camera 10, an image storage unit 201b that records images and analysis programs from the surveillance camera 10, and an analysis data recording unit 201c that records the results of analyzing the videos or images from the surveillance camera 10. The central processing unit 202 comprises a moving object detection unit 202a, an artificial intelligence determination unit 202b, a marking unit 202c, a video analysis unit 202d, and a snapshot analysis unit 202e. The moving object detection unit 202a, the artificial intelligence determination unit 202b, and the marking unit 202c are programs that analyze videos captured by the surveillance camera 10 in cooperation with the central processing unit 202. The video analysis unit 202d is a program that performs video analysis in cooperation with the central processing unit 202. The snapshot analysis unit 202e is a program that performs video analysis in cooperation with the central processing unit 202. The moving object detection unit 202a can detect moving objects using, for example, publicly available image processing software called OpenCV and a frame difference method. The artificial intelligence determination unit 202b can determine whether a detected moving object is a person and determine the person's attributes (gender, age) based on a model structure of a machine learning algorithm (for example, a convolutional neural network model structure). If the artificial intelligence determination unit 202b determines that the moving object is a person, the marking unit 202c can perform a marking process to surround the person with a rectangular frame, as shown in FIG. 1B (the video analysis unit 202d and the snapshot analysis unit 202e will be described later). The results of the analysis by the artificial intelligence server 20 can be displayed on the console PC 30 connected to the artificial intelligence server 20 (for example, the console PC 30 can display a screen in which people detected from a video or image are individually marked, as shown in FIG. 1B, which will be described later). It is also possible to display the attributes of the people (gender, age).

[0017] FIG. 1B shows a screen that appears when the central processing unit 202 analyzes a video or image and marks detected people. Such a screen can be displayed on the console PC 30. FIG. 1B shows a case where detected people are marked by surrounding them with a square frame. However, detected people may be marked in other ways (such as by surrounding them with a circle). Also, attributes of the people (gender, age) may be displayed.

[0018] FIG. 2 shows the process of video analysis when several videos (video files) are communicated between the surveillance camera 10 and the artificial intelligence server 20. First, in S1, the artificial intelligence server 20 requests the surveillance camera 10 to transfer the video files to the video storage unit 201a, and monitors whether a communication error occurs between the surveillance camera 10 and the artificial intelligence server 20. Here, in S2, a communication error occurs between the surveillance camera 10 and the artificial intelligence server 20. If a communication error occurs, in S3, the artificial intelligence server 20 requests the surveillance camera 10 to transfer the video files again within a first period (every minute in FIG. 2) after the communication error occurred, and monitors again whether a communication error occurs between the surveillance camera 10 and the artificial intelligence server 20. Meanwhile, in S4, when the transfer of the video files to the video storage unit 201a is completed, the artificial intelligence server 20 starts analyzing the video files recorded in the video storage unit 201a. Then, the artificial intelligence server 20 repeats the above-mentioned S1 of requesting the surveillance camera 10 to transfer the video file to the video storage unit 201a during the second period (every 15 minutes in FIG. 2).

[0019] FIG. 3 shows the process of communicating several snapshot images (image files) between the surveillance camera 10 and the artificial intelligence server 20 during snapshot analysis. As an example, in FIG. 3, snapshot images taken by the surveillance camera 10 every minute are recorded on the recording medium 102. First, in S5, the artificial intelligence server 20 requests the surveillance camera 10 to transfer 15 minutes' worth of image files (corresponding to, for example, 15 snapshot images taken at a first cycle of 1 minute) to the image storage unit 201b. Next, in S6, the surveillance camera 10 transfers the 15 minutes' worth of image files to the image storage unit 201b. The artificial intelligence server 20 then begins analyzing the 15 minutes' worth of image files recorded in the image storage unit 201b.

[0020] The snapshot analysis process in Figure 3 can be performed in parallel with the video analysis process in Figure 2. When both processes are performed in parallel, the analysis time can be reduced because the analysis is performed using images rather than video. However, snapshot analysis requires a different number of analysts compared to video analysis.

[0021] The overall processing flow of video analysis in the attribute analysis system 1 will be described with reference to FIG. 4. First, the artificial intelligence server 20 starts video analysis in step 400. Then, in step 410, the artificial intelligence server 20 requests the surveillance camera 10 to transfer the video recorded on the recording medium 102 to the video storage unit 201a. Then, the artificial intelligence server 20 monitors, in cooperation with the video analysis unit 202d of the central processing unit 202, whether a communication error occurs between the surveillance camera 10 and the artificial intelligence server 20. In step 420, the surveillance camera 10 reads the video requested by the artificial intelligence server 20 from the recording medium 102 and transfers it to the video storage unit 201a of the artificial intelligence server 20 via the Internet line via the communication unit 103. Here, the communication unit 103 can transfer the video wirelessly, for example, via a SIM card built into the surveillance camera 10. In step 430, the artificial intelligence server 20 determines whether the video requested in step 410 has been transferred to the video storage unit 201a. Here, if it is determined in step 430 that the video transfer has been completed (successful), the process proceeds to step 440, where an artificial intelligence analysis is performed on the requested video. In this artificial intelligence analysis, the central processing unit 202 cooperates with the moving object detection unit 202a and the artificial intelligence determination unit 202b to perform a process of detecting people from the video, cooperates with the marking unit 202c of the central processing unit 202 to perform a process of marking the detected people (marking process) and displaying their attributes, and cooperates with the video analysis unit 202d of the central processing unit 202 to perform a process of recording the number and attributes of the detected people in the analysis data recording unit 201c. Then, in step 450, the video analysis ends. On the other hand, if it is determined in step 430 that the transfer has not been completed, the process returns to step 410, and the artificial intelligence server 20 again requests the surveillance camera 10 to transfer the video recorded on the recording medium 102 to the video storage unit 201a. Here, the artificial intelligence server 20 can determine, in cooperation with the video analysis unit 202d of the central processing unit 202, whether the transfer of the requested video to the video storage unit 201a has been completed within a predetermined period (for example, every minute as a first period) after detecting the occurrence of a communication error. After the video analysis is completed, the artificial intelligence server 20 can return to step 400 and start the video analysis again at predetermined intervals (for example, every 15 minutes as the second interval).

[0022] The overall processing flow of snapshot analysis in the attribute analysis system 1 will be described with reference to FIG. 5. First, the artificial intelligence server 20 starts snapshot analysis in step 500. Then, in step 510, the artificial intelligence server 20 requests the surveillance camera 10 to transfer images captured at a predetermined interval (e.g., every minute) from the recording medium 102 to the image storage unit 201b. In step 520, the surveillance camera 10 reads the images requested by the artificial intelligence server 20 from the recording medium 102 and transfers them to the image storage unit 201b of the artificial intelligence server 20 via the Internet line via the communication unit 103. Here, the communication unit 103 can transfer images wirelessly, for example, via a SIM card built into the surveillance camera 10. In step 530, the artificial intelligence server 20 determines whether the transfer of the images requested in step 510 to the image storage unit 201b has been completed. If it is determined in step 530 that the image transfer has been completed (successful), the process proceeds to step 540, where an artificial intelligence analysis is performed on the requested image. In this artificial intelligence analysis, the artificial intelligence server 20 cooperates with the artificial intelligence determination unit 202b of the central processing unit 202 to detect people from the image, cooperates with the marking unit 202c of the central processing unit 202 to mark the detected people (marking process), and cooperates with the snapshot analysis unit 202e of the central processing unit 202 to record the number of detected people in the analysis data recording unit 201c. Then, in step 550, the artificial intelligence server 20 applies a correction value determined in advance to the number of people detected by snapshot analysis to determine the corrected number of people analyzed in snapshots, and records the corrected number of people analyzed in snapshots in the analysis data recording unit 201c. This correction value is a numerical value obtained by performing a regression analysis on the number of people detected by video analysis of videos and images taken by the same surveillance camera 10 at the same time multiple times over a predetermined period (for example, two months) and the number of people detected by snapshot analysis of the images (details of the number-of-people correction in step 550 will be explained later). Then, in step 560, the snapshot analysis is terminated. On the other hand, if it is determined in step 530 that the transfer has not been completed, the process proceeds to step 560, where the snapshot analysis is terminated. After the snapshot analysis is completed in step 560, the artificial intelligence server 20 can return to step 500 and start the snapshot analysis again at predetermined intervals (for example, every 15 minutes).

[0023] Next, referring to Figures 6 to 9, snapshot analysis, in which the correction value obtained from video analysis is applied to improve the accuracy of the number of people analyzed, will be described. Figure 6 shows measurement data that compiles the video analysis number of people obtained from video analysis and the snapshot analysis number (before correction) obtained from image analysis for each video file and image file taken at the same time (with the same timestamp) by a surveillance camera 10 (with the same camera ID "1") installed in a specified location multiple times over the two-month period from June 1, 2023 to July 31, 2023. Referring to Figure 6, it can be seen that there is a significant difference between the snapshot analysis number (before correction) and the video analysis number.

[0024] Next, referring to FIG. 7, we will explain how the artificial intelligence server 20 performs regression analysis on the measurement data of the number of people analyzing videos and the number of people analyzing snapshots (before correction) shown in FIG. 6 to determine the optimal correction value. Here, FIG. 7 shows the measurement data of FIG. 6 plotted on a graph with the number of people analyzing videos on the vertical axis (y-axis) and the number of people analyzing snapshots on the horizontal axis (x-axis). The artificial intelligence server 20 then determines the optimal correction value between the number of people analyzing videos and the number of people analyzing snapshots by linear approximation using, for example, the least squares method. As shown in FIG. 7, it can be seen that there is a relationship of y = 1.8038x between the number of people analyzing videos (y-axis) and the number of people analyzing snapshots (x-axis) shown in FIG. 6. The above correction values ​​can also be calculated by, for example, another computer that communicates with the artificial intelligence server 20 receiving the above measurement data. Also, the calculated correction values ​​can be transmitted to the artificial intelligence server 20 from the other computer.

[0025] Figure 8 shows a table of basic statistics for the measurement data in Figure 7. Referring to Figure 8, the basic statistics for video analysis (corresponding to "Video Analysis" in Figure 8) are: minimum 874, maximum 3084, median 1415, mean 1415, and standard deviation 2210. The basic statistics for snapshot analysis (corresponding to "Snapshot Analysis" in Figure 8) are: minimum 30, maximum 1748, median 769, mean 820, and standard deviation 1718. The error between the video analysis number of people and snapshot analysis number of people at the same time (corresponding to "Error (Video - Snapshot)" in Figure 8) is: minimum 234, maximum 2156, median 665, mean 704, and standard deviation 241. As such, if snapshot analysis is applied as a substitute for video analysis without correction, the error with the number of people obtained by video analysis will be large.

[0026] Figure 9 shows a graph of the error (before correction) of the snapshot analysis number (before correction) compared to the video analysis number, and the error (after correction) of the snapshot analysis number corrected using the correction value obtained from the regression analysis in Figure 7. In Figure 9, the vertical axis represents the error (number of people), and the horizontal axis represents the number of samples (the number of pairs of video analysis number and snapshot analysis number taken at the same time). Referring to Figure 9, the snapshot analysis number error (after correction) approaches zero across the entire sample number more than the snapshot analysis number error (before correction), and the difference from the video analysis number is smaller. The values ​​for the error (minimum, maximum, median, average, and standard deviation) between the snapshot analysis number (after correction) and the video analysis number are shown in the "Error Results After Correction" on the right side of Figure 9. In this "Error Results After Correction," the median is 38.0 and the average is 261.1. This value is smaller than the uncorrected value of snapshot analysis (i.e., the median value of 665 and the average value of 704 for "Error (video - snapshot)" in Figure 8). Therefore, by applying the correction value obtained from the regression analysis above to the number of people analyzed by snapshot analysis, it is possible to determine the number of people with an accuracy similar to that of video analysis while reducing the time required compared to video analysis.

[0027] As described above, the attribute analysis system 1 according to one embodiment of the present invention has been described using an example in which the number of people detected in video analysis and snapshot analysis is calculated. However, it should be understood that the attribute analysis system 1 can be applied not only to cases in which the number of people is calculated, but also to cases in which the number of vehicles (e.g., traffic volume) is calculated. For example, in the above embodiment, the analysis target can be vehicles, and the number and attributes of such vehicles (such as ordinary cars, trucks, buses, or motorcycles) can also be analyzed. [Industrial Applicability]

[0028] The attribute analysis system, which is one embodiment of the present invention, is useful in that it can achieve highly reliable data communication between a surveillance camera and an artificial intelligence server, and by applying a correction value to the number of people analyzed in a snapshot, it can analyze the number of people more accurately than analyzing images alone while reducing the time required compared to video analysis. [Explanation of symbols]

[0029] 1. Attribute analysis system 10. Surveillance Cameras 101 Power supply section 101a Battery 101b Solar Panel 102 Recording media (memory cards) 103 Communications Department 20 Artificial Intelligence Server 201 Recording Department 201a Video storage unit 201b Image storage unit 201c Analysis data recording section 202 Central processing unit 202a Motion detection unit 202b Artificial Intelligence Judgment Department 202c Marking section 202d Video Analysis Department 202e Snapshot Analysis Section 30 Control console

Claims

1. a surveillance camera including a power supply unit including a battery and a solar panel for charging the battery, a recording medium for storing captured videos and images captured in a first cycle, and a communication unit for transmitting the videos or images recorded on the recording medium; an artificial intelligence server including a video storage unit that records the video received from the recording medium, an image storage unit that records the images received from the recording medium, and a central processing unit that analyzes the video or the images to detect people and analyzes the number and attributes of the people; It is equipped with The artificial intelligence server controls the central processing unit to: For the video and the images taken multiple times at the same time during a predetermined period, the number of people detected from the video recorded in the video storage unit and the number of people detected from the images recorded in the image storage unit are calculated, and an approximate calculation is performed using a statistical method between the number of people detected from the video and the number of people detected from the images to calculate a correction value between the video and the images; The correction value is applied to the number of people detected in each of the images captured in the first period, and the number of people at the time the image was captured is calculated. An attribute analysis system comprising:

2. a surveillance camera including a power supply unit having a battery and a solar panel for charging the battery, a recording medium for storing captured video, and a communication unit for transmitting the video recorded on the recording medium; an artificial intelligence server including a video storage unit that records the video received from the recording medium, and a central processing unit that analyzes the video recorded in the video storage unit to detect people and analyzes the number and attributes of the people; It is equipped with The artificial intelligence server (a) causing the surveillance camera to transmit video images captured over a predetermined period of time to the video storage unit, and monitoring for the occurrence of a communication error between the surveillance camera and the artificial intelligence server; (b) when the video is transmitted to the video storage unit without detecting the communication error, the central processing unit is caused to analyze the video to detect people and analyze the number and attributes of the people; (c) when the communication error is detected, causing the monitoring camera to retransmit the video to the video storage unit, and monitoring the occurrence of the communication error between the monitoring camera and the artificial intelligence server during a first period; (d) repeating the steps (a) to (c) above for a second period longer than the first period; An attribute analysis system comprising:

3. The attribute analysis system according to claim 1 or 2, wherein the communication unit comprises a SIM card.

4. 3. The attribute analysis system according to claim 1, further comprising an operator console that communicates with the artificial intelligence server via wired or wireless communication using an internet line.

5. a surveillance camera including a power supply unit including a battery and a solar panel for charging the battery, a recording medium for storing captured videos and images captured at a first period, and a communication unit for transmitting the videos or images recorded on the recording medium; an artificial intelligence server including a video storage unit that records the video received from the recording medium, an image storage unit that records the images received from the recording medium, and a central processing unit that analyzes the video or the images to detect people and analyzes the number and attributes of the people; An estimation method in an attribute analysis system comprising: The artificial intelligence server controls the central processing unit to: a step of calculating the number of people detected from the video recorded in the video storage unit and the number of people detected from the images recorded in the image storage unit for the video and the images taken multiple times at the same time over a predetermined period in advance, and performing an approximate calculation using a statistical method between the number of people detected from the video and the number of people detected from the images to calculate a correction value between the video and the images; applying the correction value to the number of people detected in each of the images captured in the first period, to determine the number of people at the time the image was captured; An estimation method in an attribute analysis system, comprising:

6. a surveillance camera equipped with a power supply unit including a battery and a solar panel for charging the battery, a recording medium for storing captured video, and a communication unit for transmitting the video recorded on the recording medium; an artificial intelligence server including a video storage unit that records the video received from the recording medium, and a central processing unit that analyzes the video recorded in the video storage unit to detect people and analyzes the number and attributes of the people; A communication method in an attribute analysis system comprising: The artificial intelligence server (a') causing the surveillance camera to transmit video images captured over a predetermined period of time to the video storage unit, and monitoring for the occurrence of a communication error between the surveillance camera and the artificial intelligence server; (b') when the video is transmitted to the video storage unit without detecting the communication error, causing the central processing unit to analyze the video to detect people and analyze the number and attributes of the people; (c') when the communication error is detected, causing the surveillance camera to retransmit the video to the video storage unit, and monitoring the occurrence of the communication error between the surveillance camera and the artificial intelligence server during a first period; (d') repeating steps (a') to (c') above for a second period longer than the first period; A communication method in an attribute analysis system.

Images