Image recognition device, image recognition system, and image recognition method

Abstract

To provide an image recognition device, an image recognition system, and an image recognition method that suppress erroneous detection caused by weather changes.SOLUTION: An image recognition device includes an image recognition unit 101 that detects an object by comparing an image captured by a surveillance camera with a pre-stored reference image, and a difference non-detection mask setting unit that is an area in which differences between images are not detected. and a difference non-detection mask setting unit 102c that sets a difference non-detection mask which is an area where differences between images are not detected, according to the current weather at the place where the camera is installed. The image recognition unit 101 performs detection using a set difference non-detection mask.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to image recognition technology. [Background technology]

[0002] In order to find objects such as abandoned items, an image recognition method is known in which a reference image that does not show any objects such as abandoned items is prepared in advance, the reference image is compared with a current image captured by a surveillance camera to detect differences, and the object such as abandoned items is detected from the differences. In such a method of comparing with a reference image, in outdoor facilities, if the weather when the reference image was prepared differs from the weather when the current image was captured, false detection may occur due to reflection of sunlight or shadows.

[0003] Patent Document 1 discloses a method of setting an image quality measurement area on an image input from an imaging device, and issuing a warning when the contrast in the set image quality measurement area decreases. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2001-160146 Summary of the Invention [Problem to be solved by the invention]

[0005] However, even when image quality such as contrast is good due to reflection of sunlight or shadows, false detection occurs, and therefore the image recognition method of Patent Document 1 has the problem of being unable to deal with changes in images due to weather.

[0006] The present disclosure has been made in light of the above background, and aims to provide an image recognition technology that can reduce false detections that may occur due to changes in weather. [Means for solving the problem]

[0007] An image recognition device according to an embodiment of the present disclosure includes an image recognition unit that detects objects by comparing an image captured by a surveillance camera with a reference image stored in advance, and a difference non-detection mask setting unit that sets a difference non-detection mask, which is an area in which differences between images are not detected, depending on the weather in the location where the surveillance camera is installed, and the image recognition unit performs the detection using the set difference non-detection mask. [Effects of the Invention]

[0008] According to the image recognition device according to the embodiment of the present disclosure, it is possible to suppress false detections that may occur due to changes in weather. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram illustrating an example of the configuration of an image recognition device and an image recognition system according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating a detailed configuration example of the image recognition device according to the first embodiment. [Figure 3A] FIG. 10 is a diagram for explaining differences in an image caused by weather conditions. [Figure 3B] FIG. 10 is a diagram for explaining differences in an image caused by weather conditions. [Figure 3C] FIG. 10 is a diagram for explaining differences in an image caused by weather conditions. [Figure 3D] FIG. 10 is a diagram illustrating an example of application of a difference non-detection mask. [Figure 4A] FIG. 10 is a diagram illustrating an example of application of a difference non-detection mask. [Figure 4B] FIG. 10 is a diagram illustrating an example of application of a difference non-detection mask. [Figure 4C] FIG. 10 is a diagram illustrating an example of application of a difference non-detection mask. [Figure 5A] FIG. 2 is a diagram illustrating an example of the hardware configuration of an image recognition device. [Figure 5B] FIG. 2 is a diagram illustrating an example of the hardware configuration of an image recognition device. [Figure 6]4 is a flowchart showing the operation of the image recognition device according to the first embodiment. [Figure 7] FIG. 10 is a diagram illustrating an example of the configuration of an image recognition device according to a second embodiment. [Figure 8A] FIG. 1 is a diagram showing shadows caused by weather conditions. [Figure 8B] FIG. 10 is a diagram illustrating an example of application of a difference non-detection mask. [Figure 8C] FIG. 10 is a diagram illustrating an example of application of a difference non-detection mask. [Figure 8D] FIG. 10 is a diagram illustrating an example of application of a difference non-detection mask. [Figure 9] 10 is a flowchart showing the operation of the image recognition device according to the second embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of the configuration of an image recognition device according to a third embodiment. [Figure 11] 10 is a diagram for explaining the operation of a weather information determination unit. FIG. [Figure 12] 10 is a flowchart showing the operation of the image recognition device according to the third embodiment. [Figure 13] FIG. 10 is a diagram illustrating an example of the configuration of an image recognition device and an image recognition system according to a fourth embodiment. [Figure 14] FIG. 10 is a diagram illustrating a display example of a display screen. [Figure 15] 10 is a flowchart showing the operation of the image recognition device according to the fourth embodiment. [Figure 16] FIG. 13 is a diagram illustrating an example of the configuration of an image recognition device according to a fifth embodiment. [Figure 17] 10 is a flowchart showing the operation of the image recognition device according to the fifth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Various embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that components with the same or similar reference numerals in the drawings have the same or similar configurations or functions, and redundant descriptions of such components will be omitted.

[0011] Embodiment 1 <Image recognition system configuration> An image recognition device and an image recognition system according to a first embodiment of the present disclosure will be described with reference to FIGS. 1 to 5. FIG. 1 is a diagram illustrating an example configuration of an image recognition device and an image recognition system according to the first embodiment. As illustrated in FIG. 1, an image recognition system 6a includes, as an example, an image recognition device 100, surveillance cameras 3a and 3b, a monitoring terminal 4, and an on-premise network 5. The image recognition system 6a detects various events within a facility to be monitored using images from surveillance cameras 3 (3a, 3b) installed in the facility and notifies facility staff of the detected events via the monitoring terminal 4. Examples of the facility include transportation stations such as train and bus stations, parking lots, amusement parks, and public parks. Images captured by the surveillance cameras 3 are transmitted to the image recognition device 100 via the on-premise network 5 within the facility. The image recognition device 100 analyzes the transmitted images to detect various conditions occurring within the facility. Examples of conditions detected by the image recognition system include the detection of abandoned objects within the facility, people lingering for long periods of time, bringing in dangerous objects, and people falling onto or entering the tracks at railway station facilities operated by railway operators. The conditions detected through image analysis by the image recognition device 100 are displayed on the display device of the monitoring terminal 4, and the detected conditions are notified to facility staff.

[0012] The image recognition system 6a is connected to a system of a weather information provider 9 via a network 7 and the Internet 8, and acquires weather information 10 indicating the weather from the weather information provider 9. A plurality of image recognition systems 6a may be connected to the network 7.

[0013] <Configuration of image recognition device> 2 is a diagram showing a detailed configuration example of the image recognition device 100. As shown in FIG. 2, the image recognition device 100 includes an image recognition unit 101 and a weather information analysis unit 102.

[0014] (Image Recognition Unit) The image recognition unit 101 analyzes images captured by the surveillance camera 3 to detect various objects, such as people, in the images. For example, it detects abandoned objects in the images. There are various image analysis methods, one of which is to compare a reference image with a current image and detect the difference between them. For example, by comparing the reference image with the current image to detect a difference, if the difference continues to be observed, it can be determined that the difference represents an abandoned object and can be detected as such. This reference image must not include facility users, such as passengers in the case of a station, so an image taken early in the morning, outside the facility's operating hours, is used. This image taken early in the morning, for example, is stored in a memory (not shown) as the reference image, and the image recognition unit 101 detects objects by comparing the reference image with the current image captured by the surveillance camera 3.

[0015] In the method of comparing a reference image with a current image, the cause of false positives when recognizing images of outdoor facilities is the unique images that are displayed due to weather conditions and other factors. This will be explained with reference to Figure 3. As shown in Figure 3, in contrast to the reference image (reference image in Figure 3A), which is often taken early in the morning, there are differences such as the shadow of the facility or the reflection of sunlight on sunny days (image on sunny days in Figure 3B), and the reflection of lighting caused by the wetness of the platform floor on rainy days (image on rainy days in Figure 3C). The shadows or reflections in these images are not abandoned objects that should be determined as detection targets, but because they continue to be seen, they are falsely detected as abandoned objects.

[0016] One possible countermeasure to such false detections is to set a difference non-detection mask. A difference non-detection mask is a region (area) surrounded by an arbitrary shape set within the image analyzed by the image recognition device 100. Even if an image different from the reference image is detected within the set region, the different image is not detected as a difference. As shown in the example of the difference non-detection mask application in Figure 3D, applying a difference non-detection mask to all areas where unique images caused by weather, such as the aforementioned shadows or reflections, may be observed can prevent false detections due to weather. However, when using such a method, the difference non-detection region is set large or wide, which reduces the range in which differences can be detected, thereby reducing the accuracy of condition detection by the image recognition device.

[0017] (Weather Information Analysis Department) To solve this problem, the image recognition device 100 of the present disclosure adaptively sets a difference non-detection mask according to the climate. To adaptively set a difference non-detection mask according to the climate, the weather information analysis unit 102 includes a weather information acquisition unit 102a, a weather determination unit 102b, a difference non-detection mask setting unit 102c, and a mask information output unit 102d, as shown in FIG.

[0018] (Weather Information Acquisition Department) The weather information acquisition unit 102a acquires weather information 10 at regular intervals. As an example, the weather information 10 is information provided by an external weather information provider 9 that provides weather information, and is information indicating weather conditions such as sunny, cloudy, rainy, or snowfall that match the area where the target facility, where the surveillance camera 3 is installed, is located. The weather information acquisition unit 102a acquires the weather information 10 from the weather information provider 9 at regular intervals via the connected network 7. Note that the regular interval shown here is assumed to be about one hour, which allows for a period of time to respond to changes in the weather, but can be set arbitrarily by the weather information analysis unit 102.

[0019] (Weather Judgment Department) The weather determination unit 102b determines the weather at the target facility from the weather information 10 acquired by the weather information acquisition unit 102a. As described above, the weather information 10 is acquired at regular intervals, so the weather determination unit 102b can determine the current climate for a certain time interval.

[0020] (Difference non-detection mask setting section) The difference non-detection mask setting unit 102c dynamically sets the difference non-detection mask. That is, the difference non-detection mask setting unit 102c sets the difference non-detection mask according to the current weather determined by the weather determination unit 102b. The difference non-detection mask setting unit 102c sets multiple difference non-detection masks in one detection target image and independently specifies whether each mask is valid or invalid. This will be described with reference to FIG. 4.

[0021] As shown in FIG. 4A, the difference non-detection mask setting unit 102c sets multiple difference non-detection masks M1 to M4 at the respective positions of unique differences that occur due to weather. On sunny days, as shown in FIG. 4B, the difference non-detection mask setting unit 102c enables difference non-detection masks M1, M3, and M4 to mask sunlight reflections and facility shadows. On rainy days, as shown in FIG. 4C, the difference non-detection mask setting unit 102c enables difference non-detection mask M2 to mask lighting reflections caused by a wet platform floor. In this way, the difference non-detection mask setting unit 102c sets multiple difference non-detection masks within a single detection target image and independently specifies whether each mask is enabled or disabled.

[0022] (Mask information output section) The mask information output unit 102d outputs the setting information of the difference non-detection mask set by the difference non-detection mask setting unit 102c to the image recognition unit 101 as mask information.

[0023] The image recognition unit 101 performs image analysis using the enabled difference non-detection mask indicated by the mask information output from the mask information output unit 102d. That is, the image recognition unit 101 refers to the enabled difference non-detection mask and performs image analysis without detecting, as a difference, any fluctuation in the area masked by the difference non-detection mask in the image captured by the surveillance camera 3.

[0024] Next, the hardware configuration of the image recognition device 100 will be described with reference to FIGS. 5A and 5B. As an example, as shown in FIG. 5A, the image recognition device 100 is realized by a processing circuit 100a. The processing circuit 100a realizes the functions of the functional units included in the image recognition device 100. The processing circuit 100a is, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof. The functions of each functional unit of the image recognition device 100 may be realized by separate processing circuits, or these functions may be realized together by a single processing circuit.

[0025] As another example, as shown in FIG. 5B, the image recognition device 100 is realized by a processor 100b and a memory 100c. The functions of the functional units of the image recognition device 100 are realized by the processor 100b reading and executing a program stored in the memory 100c. The program is realized as software, firmware, or a combination of software and firmware. Examples of the memory 100c include non-volatile or volatile semiconductor memory such as a random access memory (RAM), a read-only memory (ROM), a flash memory, an erasable programmable read-only memory (EPROM), or an electrically EEPROM (EEPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a minidisk, and a DVD.

[0026] <Operation> Next, the operation of the image recognition device 100 will be described with reference to Fig. 6. In step S51, the weather information acquisition unit 102a acquires weather information 10 from an external weather information provider.

[0027] In step S52, the weather determining unit 102b determines the current weather indicated by the weather information 10.

[0028] In step S53, the difference non-detection mask setting unit 102c sets the difference non-detection mask assigned to the image specific to each weather to be valid according to the determined weather.

[0029] In step S54, the mask information output unit 102d outputs information indicating the enabled difference non-detection mask as mask information to the image recognition unit 101. The image recognition unit 101 performs image recognition using the enabled difference non-detection mask. When comparing the current image with the reference image, the range of the enabled difference non-detection mask is not detected as a difference even if there is a fluctuation, so that erroneous detection due to weather-specific images can be prevented.

[0030] Thereafter, in step S55, after a certain period of time has elapsed, the process returns to step S51.

[0031] In this operation, since the judgment is performed at regular intervals, it is possible to set a difference non-detection mask appropriate for that time, thereby preventing the setting of an unnecessary difference non-detection mask and unnecessarily narrowing the detection range of image recognition.

[0032] This operation can also be used for all content that can be detected using the difference from a reference image, and in addition to detecting abandoned objects, it can also be used to detect people staying behind, or, in the case of station facilities, people falling onto or entering the tracks.

[0033] Embodiment 2 <Configuration> Next, an image recognition device 100A according to a second embodiment of the present disclosure will be described with reference to Figures 7 and 8. Figure 7 is a diagram illustrating an example configuration of image recognition device 100A. Image recognition device 100A includes a weather information analysis unit 102A, which additionally includes a shadow position calculation unit 102e that calculates the position of a shadow of equipment compared to weather information analysis unit 102 of the first embodiment.

[0034] (Shadow position calculation part) The shadow position calculation unit 102e stores location information indicating the latitude and longitude of the facility to be detected in a memory (not shown), and calculates the position of the shadow for each hour in the image of the image recognition target based on the position of the sun determined by the date and time and the location information of the facility. As shown in Figure 8A, the position of the shadow is different between 10:00 on X-month, X-day, X-year, ...

[0035] In the second embodiment, the difference non-detection mask setting unit 102c prepares a plurality of difference non-detection masks with different setting positions. For example, as shown in the difference non-detection mask setting example of FIG. 8B, a plurality of difference non-detection masks, such as difference non-detection masks M11 and M12 with different setting positions, are prepared. The difference non-detection mask setting unit 102c activates the difference non-detection mask that masks the area closest to the calculated shadow position. For example, if it is 10:00 on XX year, X month, X day, as shown in FIG. 8C, the difference non-detection mask M11 is activated. Also, if it is 16:00 on XX year, X month, X day, as shown in FIG. 8D, the difference non-detection mask M12 is activated. It is determined that the masked area of the difference non-detection mask M11 is closer to the shadow at 11:00 on XX year, X month, X day than the masked area of the difference non-detection mask M12, and the difference non-detection mask M11 is activated. It is determined that the mask area of the difference non-detection mask M12 is closer to the shadow of 15:00 on the Xth, Xth month of the XX year than the mask area of the difference non-detection mask M11, and the difference non-detection mask M12 is set to be valid.

[0036] This allows the most effective difference non-detection mask to be set for shadows from equipment that change depending on the season and the time of day. For example, when the weather is fine but there are no shadows from equipment on the screen, it is possible to avoid setting an unnecessary difference non-detection mask to be effective, thereby preventing the detection range of image recognition from being narrowed more than necessary.

[0037] <Operation> Next, the operation of image recognition device 100A will be described with reference to Fig. 9. Fig. 9 is a flowchart showing the operation of image recognition device 100A. The flowchart in Fig. 9 is a flowchart in which steps S83 to S85 for dynamically changing the position of the mask on fine weather are added to the flowchart in Fig. 6 according to the first embodiment.

[0038] As in the case of FIG. 6, in step S81, the weather information acquisition unit 102a acquires weather information, and in step S82, the weather determination unit 102b determines the weather.

[0039] If the current weather is determined to be fine, then in step S84, the shadow position calculation unit 102e calculates the position of the shadow of the target equipment in the facility on the relevant date and time.

[0040] In step S85, based on the position of the shadow calculated in step S84, the difference non-detection mask setting unit 102c sets, as valid, one of the multiple difference non-detection masks prepared and stored, which masks the location closest to the calculated position of the shadow (for example, a setting example of XX year, X month, X day, 10:00 in FIG. 8C, and a setting example of XX year, X month, X day, 16:00 in FIG. 8D).

[0041] In step S87, the mask information output unit 102d outputs information indicating the enabled difference non-detection mask to the image recognition unit 101 as mask information. The image recognition unit 101 performs image recognition using the enabled difference non-detection mask. This makes it possible to set the most effective difference non-detection mask for the shadow of a facility, which changes depending on the season and the date and time. Additionally, if the weather is clear but the shadow of a facility does not exist on the screen due to the position of the sun, an unnecessary difference non-detection mask will not be set, making it possible to prevent a reduction in the detection range of image recognition.

[0042] If the result in step S83 is determined to be other than fine weather, in step S86, the difference non-detection mask setting unit 102c sets the difference non-detection mask to be valid according to weather other than fine weather, as in the first embodiment.

[0043] Thereafter, similarly to the first embodiment, after a certain period of time has elapsed, the process returns to step S81 (step S88).

[0044] By providing the shadow position calculation unit 101e as in the image recognition device 100A of the second embodiment, it is possible to set a difference non-detection mask more accurately, thereby enabling more accurate object detection.

[0045] Embodiment 3 <Configuration> Next, an image recognition device 100B according to a third embodiment of the present disclosure will be described with reference to Figures 10 and 11. Figure 10 is a diagram illustrating an example configuration of the image recognition device 100B according to the third embodiment. The image recognition device 100B includes a weather information analysis unit 102B, which includes a weather information determination unit 102f that determines the weather conditions of the facility where the monitoring cameras 3 (3a, 3b) are installed, instead of the weather information acquisition unit 102a of the first embodiment.

[0046] (Weather Information Judgment Department) The weather information determination unit 102f acquires outdoor images of the facility where the monitoring cameras 3 (3a, 3b) are installed from the monitoring cameras 3 (3a, 3b). The number of monitoring cameras capturing outdoor images may be one or more. As shown in FIG. 11, the weather information determination unit 102f compares the current image acquired from the monitoring camera 3 with multiple weather images pre-stored in the weather information analysis unit 102B or the image recognition device 100B to determine the current weather. FIG. 11 shows an example in which the current image is compared with stored images including images of sunny weather, cloudy weather, rainy weather, and stormy weather, and the current weather is determined to be "cloudy." Weather can be determined based on, for example, the presence or absence of shadows on a sunny day, the presence or absence of sunlight reflection in a specific location, or color changes due to wet floors or ground on a rainy day, but other factors may also be taken into consideration.

[0047] Information indicating the weather determined by the weather information determination unit 102f is output as weather information to the weather determination unit 102b. Based on this weather information, the weather determination unit 102b, the difference non-detection mask setting unit 102c, and the mask information output unit 102d function in the same manner as in the first embodiment.

[0048] <Operation> Next, the operation of image recognition device 100B will be described with reference to Fig. 12. Fig. 12 is a flowchart showing the operation of image recognition device 100B. Fig. 12 differs from Fig. 6 in that step S111 is provided instead of step S51 in Fig. 6 according to embodiment 1. In Fig. 12, instead of acquiring weather information as in step S51 in Fig. 6, the weather information determination unit 102f determines the weather from the image and uses it as weather information.

[0049] Steps S112 to S115 in FIG. 12 are the same as steps S52 to S55 in FIG. 6, and the difference non-detection mask is set to be valid or invalid depending on the weather, and the difference non-detection mask that is set to be valid is set in the image recognition unit 101.

[0050] In the third embodiment, when the weather is determined to be fine, it is possible to add the shadow position calculation unit 102e shown in the second embodiment and dynamically change the position of the shadow of the facility.

[0051] In addition, by combining embodiment 3 and embodiment 1, it is possible to comprehensively determine weather information from weather information obtained from an external weather information provider 9 and weather information determined from outdoor footage of the facility itself.

[0052] Embodiment 4 <Configuration> Next, an image recognition system 6b according to a fourth embodiment of the present disclosure will be described with reference to Fig. 13 and Fig. 14. Fig. 13 is a diagram illustrating an example configuration of the image recognition system 6b according to the fourth embodiment. As shown in Fig. 13, the image recognition system 6b includes a monitoring terminal 4A, which includes a weather information input unit 13 that accepts user input of weather information indicating the weather. In the image recognition system 6a according to the first embodiment, the weather information 10 is obtained from an external weather information provider 9, but the image recognition system 6b according to the fourth embodiment obtains weather information from the weather information input unit 13.

[0053] FIG. 14 shows an example of a display screen displayed on the display device of the monitoring terminal 4A. The display screen includes, for example, an outdoor image display screen that displays outdoor images to the facility employee (user), a weather information output screen that displays current weather information determined by the weather information input system, and a weather information input screen that allows the facility employee to input weather information. Corresponding to the weather information input screen, the monitoring terminal 4A is provided with a weather information input unit 13. The facility employee determines the current weather by checking the outdoor image and inputs weather information via the weather information input screen. If the employee determines that the weather displayed on the weather information output screen is the same as the current weather, they can choose not to input weather information. This input can be performed at any time. In the fourth embodiment, the difference non-detection mask setting unit 102c sets a difference non-detection mask according to the weather indicated by the weather information input by the user. The hardware configuration of the monitoring terminal 4 (4A) can be the same as that shown in FIG. 5A or 5B.

[0054] <Operation> Next, the operation of the image recognition system 6b will be described with reference to Fig. 15. Fig. 15 is a flowchart showing the operation of the image recognition system 6b. Steps S141 to S144 in Fig. 15 are substantially the same as steps S51 to S54 in Fig. 6 according to the first embodiment. The only difference is where the weather information is acquired from. That is, in step S141 in Fig. 15, the weather information acquisition unit 102a acquires weather information from the weather information input unit 13.

[0055] In Fig. 15, it is assumed that the weather information is judged without waiting for a certain period of time to elapse, each time it is input by the weather information input unit 13. In other words, the flowchart in Fig. 15 does not include a step corresponding to step 55 in Fig. 6.

[0056] In the fourth embodiment, when the weather is determined to be fine, it is possible to add the shadow position calculation unit 102e shown in the second embodiment and dynamically change the position of the shadow of the facility.

[0057] It is also possible to combine the fourth embodiment with the first and second embodiments. In this case, priority is given to input by facility personnel through the weather information input unit 13, and if there is no input for a certain period of time, the current weather is determined to be external weather information, weather information determined from video of the outside of the facility, or weather information determined comprehensively from both of these pieces of information.

[0058] Embodiment 5. <Configuration> Next, an image recognition device 100C according to a fifth embodiment of the present disclosure will be described with reference to Fig. 16. Fig. 16 is a diagram illustrating an example configuration of the image recognition device 100C. As shown in Fig. 16, the image recognition device 100C includes a weather information analysis unit 102C, which additionally includes a stop instruction unit 102g that instructs the weather information analysis unit 102 of the first embodiment to stop the image recognition process (difference detection).

[0059] (Stop instruction part) The stop instruction unit 102g instructs the image recognition unit 101 to stop difference detection when the weather determined by the weather determination unit 102b is a rainstorm. <Operation> FIG. 17 is a flowchart showing the operation according to the fifth embodiment. FIG. 17 adds processing for when the weather is a rainstorm to FIG. 6 according to the first embodiment. In the case of a rainstorm caused by a typhoon or the like, rain may fall on a surveillance camera capturing outdoor footage, making it impossible to compare the captured image with a reference image. To deal with such a case, when the weather is a rainstorm, difference detection itself is stopped rather than a difference non-detection mask being set. This makes it possible to prevent false detections that occur when comparison with a reference image is impossible.

[0060] The operation will be described with reference to Fig. 17. If the weather determined in step S152 is a rainstorm, the difference non-detection mask is not enabled or disabled, but the image recognition unit 101 is caused to stop difference detection itself in step S154. The other steps are the same as those in Fig. 6.

[0061] Storm information may be typhoon information or heavy rain warnings provided by an external weather information provider as in embodiment 1, or may be determined from external video footage as in embodiment 2, or may be input from personnel as in embodiment 3.

[0062] <Additional Notes> Some aspects of the various embodiments described above are summarized below.

[0063] (Appendix 1) The image recognition device (100; 100A; 100B; 100C) of Appendix 1 comprises an image recognition unit (101) that detects objects by comparing an image captured by a surveillance camera with a reference image stored in advance, and a difference non-detection mask setting unit (102c) that sets a difference non-detection mask, which is an area where differences between images are not detected, according to the current weather at the location where the surveillance camera is installed, and the image recognition unit performs the detection using the set difference non-detection mask.

[0064] (Appendix 2) The image recognition device (100; 100A; 100C) of Appendix 2 is the image recognition device of Appendix 1, further comprising a weather information acquisition unit (102a) that acquires weather information indicating the weather, the weather information being information provided by a weather information provider via a network, and the difference non-detection mask setting unit sets the difference non-detection mask according to the weather indicated by the acquired weather information.

[0065] (Appendix 3) The image recognition device (100B) of Appendix 3 is the image recognition device of Appendix 1, further comprising a weather information determination unit (102f) that determines weather information indicating the weather from an image captured by a surveillance camera, and the difference non-detection mask setting unit sets the difference non-detection mask according to the weather indicated by the weather information determined from the image.

[0066] (Appendix 4) The image recognition device (100; 100A; 100B; 100C) of Supplementary Note 4 is the image recognition device of any one of Supplementary Notes 1 to 3, and further includes a shadow position calculation unit (102e) that calculates the position of the shadow of the equipment from position information of latitude and longitude where the equipment is located and the position of the sun based on date and time, and the difference non-detection mask setting unit changes the position of the difference non-detection mask in accordance with the calculated shadow position when the weather is fine.

[0067] (Appendix 5) The image recognition device (100; 100A; 100B; 100C) of Supplementary Note 5 is the image recognition device of any one of Supplementary Notes 1 to 4, and further includes a stop instruction unit (102g) that instructs the image recognition unit to stop detecting differences when the weather is a storm.

[0068] (Appendix 6) The image recognition system (6a; 6b) of Appendix 6 comprises a monitoring terminal (4A) having a weather information input unit (13) that accepts user input of weather information indicating the weather, and any one of the image recognition devices of Appendix 1 to 5, and the difference non-detection mask setting unit sets the difference non-detection mask according to the weather indicated by the weather information input by the user.

[0069] (Appendix 7) The image recognition method of Appendix 7 is an image recognition method using an image recognition device having a difference non-detection mask setting unit and an image recognition unit, and includes a step (step S53) in which the difference non-detection mask setting unit sets a difference non-detection mask, which is an area in which differences between images are not detected, in accordance with the current weather, and a step (step S54) in which the image recognition unit uses the set difference non-detection mask to compare an image taken by a surveillance camera with a reference image stored in advance to detect an object, and the difference non-detection mask is a difference non-detection mask set in accordance with the current weather at a location where the surveillance camera is installed.

[0070] It is possible to combine the embodiments, and to modify or omit each embodiment as appropriate. [Industrial Applicability]

[0071] The image recognition device or image recognition system of the present disclosure can be used in a system that performs image recognition of outdoor facilities such as railway or bus transportation stations, parking lots, amusement parks, and public parks using images captured by surveillance cameras. [Explanation of symbols]

[0072] 3 (3a, 3b) surveillance camera, 4 (4A) surveillance terminal, 5 in-house network, 6 (6a, 6b) image recognition system, 7 network, 9 weather information provider, 10 weather information, 13 weather information input unit, 100 (100A, 100B, 100C) image recognition device, 100a processing circuit, 100b processor, 100c memory, 101 image recognition unit, 102 (102A, 102B, 102C) weather information analysis unit, 102a weather information acquisition unit, 102b weather determination unit, 102c difference non-detection mask setting unit, 102d mask information output unit, 102e shadow position calculation unit, 102f weather information determination unit, 102g stop instruction unit, M (M1, M11, M12, M2, M3, M4) difference non-detection mask.

Claims

1. an image recognition unit that detects an object by comparing an image captured by a surveillance camera with a reference image stored in advance; a difference non-detection mask setting unit that sets a difference non-detection mask, which is an area in which differences between images are not detected, in accordance with the current weather at a location where the surveillance camera is installed; Equipped with the image recognition unit performs the detection using a set difference non-detection mask; An image recognition device, a shadow position calculation unit that calculates the position of a shadow of the facility based on the latitude and longitude position information of the facility and the position of the sun according to date and time; the difference non-detection mask setting unit changes the position of the difference non-detection mask in accordance with the calculated position of the shadow when the weather is fine. Image recognition device.

2. A weather information acquisition unit that acquires weather information indicating weather conditions is further provided, the weather information is provided by a weather information provider via a network, the difference non-detection mask setting unit sets the difference non-detection mask according to the weather indicated by the acquired weather information; 2. The image recognition device according to claim 1.

3. The device further includes a weather information determination unit that determines weather information from an image captured by the monitoring camera, the difference non-detection mask setting unit sets the difference non-detection mask in accordance with the weather indicated by the weather information determined from the image; 2. The image recognition device according to claim 1.

4. a stop instruction unit that instructs the image recognition unit to stop detecting the difference when the weather is a heavy rainstorm; 4. The image recognition device according to claim 1.

5. a monitoring terminal including a weather information input unit that accepts user input of weather information indicating the weather; An image recognition device according to any one of claims 1 to 4; Equipped with the difference non-detection mask setting unit sets the difference non-detection mask in accordance with the weather indicated by the weather information input by the user; Image recognition system.

6. An image recognition method using an image recognition device comprising a shadow position calculation unit, a difference non-detection mask setting unit, and an image recognition unit, a step in which the shadow position calculation unit calculates the position of the shadow of the facility from the position information of latitude and longitude where the facility is located and the position of the sun based on date and time; a step in which the difference non-detection mask setting unit sets a difference non-detection mask, which is an area in which a difference between images is not detected, in accordance with current weather, and when the weather is fine, changes the position of the difference non-detection mask in accordance with the calculated position of the shadow; the image recognition unit uses the set difference non-detection mask to compare the image captured by the surveillance camera with a reference image stored in advance to detect an object; An image recognition method comprising: The difference non-detection mask is a difference non-detection mask set according to the current weather at a location where the surveillance camera is installed. Image recognition methods.

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