JPWO2017126072A1 - Image recognition system, camera state estimation device, and storage medium - Google Patents

Abstract

An image recognition system having a camera and a camera state estimation device, wherein the camera state estimation device estimates a feature amount of an image to be detected, a size of the detection target in real space, and a parameter indicating a camera state And a target similar to the detection target is extracted from the image captured by the camera based on the feature amount, the size of the detection target in the real space, the estimated value of the parameter, and the similar target Based on the position and size in the image, an index indicating the magnitude of the error of the parameter estimation value is calculated, and when the calculated index indicating the magnitude of error does not satisfy the predetermined condition, Change the estimate.

Description

  The present invention relates to a technique for estimating the state of a camera from an image taken by the camera.

  As a background art of the present invention, there is a technique disclosed in Japanese Patent Laid-Open No. 2008-275391 (Patent Document 1). This publication describes “a position / orientation measurement apparatus that measures a relative position / orientation between an imaging apparatus that images one or more measurement target objects and the measurement target object using a three-dimensional model of the measurement target object”. Acquires a captured image by the imaging device, and projects each geometric feature in the three-dimensional model on the captured image based on the position and orientation of the imaging device to obtain a projected geometric feature. A projected geometric feature to be used for position / orientation calculation is selected from the geometric features based on the distance between the projected geometric features on the captured image, and the selected projected geometric feature is selected from the captured image. The relative position and orientation between the imaging device and the measurement target object are calculated using the image geometric feature corresponding to the projected geometric feature ”(see the summary).

JP 2008-275391 A

  In the process of detecting an object shown in an image from a camera image and recognizing the state of the object, the camera state (camera posture, angle of view) at the time of image capture may be required. For example, when it is desired to measure the speed of a person appearing on the camera, the above-described camera state is required when converting the movement distance of the person in the screen at a certain time into the movement distance in the real space.

  A technique for estimating a camera state is generally widely known as a camera calibration technique. Conventionally, camera calibration is generally performed by photographing a specific detection target having a known size and pattern, such as a checkerboard, and estimating the camera state from how the detection target is reflected. However, this method is not practical because it is necessary to recapture a specific detection target one by one in a situation where the state of the camera changes.

  Patent Document 1 described above describes a method for estimating the camera state one by one by comparing data stored in a database with a subject to be photographed. According to this method, a database that holds a three-dimensional state of a specific detection target is prepared, and the current state of the detection target is obtained by comparing the database with the camera image, thereby displaying a detection target. The camera state is estimated one by one. However, in this method, it is necessary to acquire the three-dimensional information to be detected from CAD and save it in a database. Since CAD data is created from the target design drawing data or measurement data obtained by accurately measuring the target, it is necessary to accurately measure the target when inputting the detection target information into the database. Since there is a need to exist, there is a problem that it takes time to input information and a target cannot be freely selected.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to estimate the camera state one by one using a database holding simpler information.

  In order to solve the above-described problem, one aspect of the present invention is an image recognition system including a camera and a camera state estimation device connected to the camera via a network. The camera state estimation device includes: A first interface connected to the network, a first processor connected to the first interface, and a first storage device connected to the first processor, the first storage device comprising: A feature amount of a detection target image, a size of the detection target in real space, and an estimated value of one or more parameters indicating a state of the camera are stored, and the camera captures an image and captures the camera. The first processor transmits the image to the state estimation device based on the feature quantity stored in the first storage device from the image captured by the camera; Extracting similar objects, based on the size of the detection object in real space, the estimated value of the one or more parameters, and the position and size of the similar object in the image Calculating an index indicating the magnitude of the error of the estimated value of the parameter, and changing the estimated value of the one or more parameters when the calculated index indicating the magnitude of the error does not satisfy a predetermined condition It is characterized by that.

  According to one embodiment of the present invention, a camera state can be estimated using a database holding simpler information.

It is explanatory drawing of the outline of the camera state estimation apparatus of Example 1 of this invention. It is explanatory drawing of the imaging environment in Example 1 of this invention. It is a block diagram which shows the hardware constitutions of the image recognition system of Example 1 of this invention. It is explanatory drawing of an example of the database for camera state estimation in Example 1 of this invention. It is the block diagram which showed the structural example of the image matching process part of Example 1 of this invention. It is the block diagram which showed the structural example of the camera state estimation part of Example 1 of this invention. It is a flowchart of the image matching process of Example 1 of this invention. It is a flowchart of the camera state estimation process of Example 1 of this invention. It is a flowchart of the database update process for state estimation of Example 1 of this invention. It is explanatory drawing of an example of the display in the database update apparatus of Example 1 of this invention. It is a block diagram which shows the hardware constitutions of the image recognition system of Example 2 of this invention. It is explanatory drawing of an example of the database for camera state estimation in Example 3 of this invention.

  Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. For clarity of explanation, the following description and drawings are omitted and simplified as appropriate. Moreover, in each drawing, the same code | symbol is attached | subjected to the same element and the duplication description is abbreviate | omitted as needed for clarification of description.

  FIG. 1 is a schematic explanatory diagram of a camera state estimation apparatus according to a first embodiment of the present invention.

  In FIG. 1, a camera 101 is connected to the camera state estimation apparatus 100. The camera 101 captures an image, and the image acquisition unit 102 acquires the captured image. An image 103 is an example of an image acquired by the image acquisition unit 102. In the image 103, there are several objects (objects 107, 108, and 109 in this example) on the ground 106. The camera state estimation apparatus 100 is accompanied by a storage device 104, and a camera state estimation database 105 is held in the storage device 104. The camera state estimation device 100 includes an image matching processing unit 110 and a camera state estimation unit 111 in addition to the image acquisition unit 102 and the storage device 104 described above. An image 103 captured by the camera 101 and a camera state estimation database 105 are input to the image matching processing unit 110. The image matching processing unit 110 checks whether or not the input image 103 includes a detection target image in the database. If it is included, information on the position on the image and the size of the target is extracted and output. . When the processing result of the image matching processing unit 110 is input, the camera state estimation unit 111 estimates and outputs the camera state 112 from the input information.

  FIG. 2 is an explanatory diagram of a shooting environment according to the first embodiment of the present invention.

  The camera 101 is installed at a fixed installation height 202 so as to face an angle 201 with respect to the ground 106. In addition, the camera 101 captures a range of the viewing angle 203. In the first embodiment, when the camera 101 is installed at an angle at which the ground 106 is reflected horizontally (in other words, the upper and lower sides of the rectangular image 103 captured by the camera 101 and the horizontal line of the landscape captured by the camera 101) (When the camera 101 is installed so that the two are parallel to each other). It is assumed that the object 206 to be photographed is in contact with the ground 106. Information about the size of the object, such as the height 205 to the object reference point 204, is held in the state estimation database.

  An example of the camera 101 in the situation shown in FIG. 2 is a movable surveillance camera or the like installed on a ceiling or a pillar. The movable surveillance camera can change the direction (azimuth angle) of the camera and the tilt (decoration angle or elevation angle) with respect to the ground, and the angle of view (viewing angle 203) is changed by the zoom function. The first embodiment will be described assuming a camera such as a surveillance camera.

  Note that the camera state in the present embodiment includes one or more parameters indicating the state of the camera 101, such as the camera direction (azimuth angle, depression angle, elevation angle), field angle, and installation position. For example, at least one of the parameters may be variable, such as the angle of view of the camera 101 having a zoom function and the direction of the camera 101 having a swing function. Regardless of whether it is variable or fixed, the value of at least one parameter is unknown, and the value is estimated by the camera state estimation apparatus 100. These parameters are used for an image recognition processing device 603 (to be described later) to perform arbitrary image recognition processing (for example, tracking of a photographed object).

  An X axis 207, a Y axis 208, and a Z axis 209 in FIG. 2 respectively correspond to coordinate axes of real space coordinates used in the description of the present embodiment, and are points 210 on the ground just below the camera (that is, a vertical line passing through the camera 101). And the ground) is the origin of the coordinate system. Further, the foot coordinates 211 are real space coordinates of a point on the ground just below the reference point 204 of the object 206. When the object 206 is a human, the coordinates of the human grounding point (that is, the foot) are usually used. is there.

  In this embodiment, real space coordinate values and image coordinate values are handled. As shown in FIG. 2, the coordinate value of the real space is a coordinate value of a three-dimensional coordinate system having any point in the real space photographed by the camera 101 as the origin. On the other hand, the coordinate value on the image is a coordinate value of a two-dimensional coordinate system with any point on the photographed image as the origin, and the unit is, for example, a pixel.

  FIG. 3 is a block diagram illustrating a hardware configuration of the image recognition system according to the first embodiment of this invention.

  The image recognition system includes a database update device 601, a camera state estimation device 100, an image recognition processing device 603, and a camera 101, which are connected to each other via a network 604.

  The database update device 601 includes a processor 621, a memory 622, a network interface (I / F) 623, an input terminal 607 and a display device 608.

  The processor 621 implements various functions of the database update device 601 described below by executing a program stored in the memory 622.

  The memory 622 stores a database information setting unit 609, a camera state estimation signal output unit 610, a database information acquisition unit 611, and an image acquisition unit 617, which are program modules executed by the processor 621. In the following description, each of these units is treated as a functional block and processing executed by each unit will be described. Actually, these processings are executed by the processor 621 according to instructions described in each program module. At this time, the processor 621 controls the network I / F 623, the input terminal 607, the display device 608, and the like as necessary to perform data communication and input / output by each functional block.

  FIG. 3 shows an example in which each general-purpose processor 621 implements each functional block by executing a program stored in the memory 622. However, any part or all of these functional blocks may be ASIC (Application Specific Integrated). It may be realized by dedicated hardware such as Circuit).

  The network I / F 623 is an interface that is connected to the network 604 and performs communication with other devices via the network 604.

  The display device 608 is a display device for characters and images, such as a liquid crystal display device. The input terminal 607 includes an input device that can input data from the user, such as a keyboard, a mouse, or a touch panel. For example, like the liquid crystal touch panel, the display device 608 and the input terminal 607 may be integrated.

  The database information acquisition unit 611 acquires information from the camera state estimation database 105 in the camera state estimation device 100, converts the information into a format that can be displayed on the display device 608, and displays it. The image acquisition unit 617 acquires an image from the camera 101 and outputs it to the display device 608. The database information setting unit 609 generates update information of the camera state estimation database 105 according to the operation information input from the input terminal 607 and transmits the update information to the camera state estimation apparatus 100. The camera state estimation signal output unit 610 generates a signal for starting camera state estimation processing in the camera state estimation apparatus 100 according to the operation information input from the input terminal 607 and transmits the signal to the camera state estimation apparatus 100.

  The camera state estimation device 100 includes a processor 624, a memory 625, a network I / F 626, and a storage device 104. The processor 624 implements various functions of the camera state estimation apparatus 100 described below by executing a program stored in the memory 625. The memory 622 stores an image acquisition unit 102, an image matching processing unit 110, a camera state estimation unit 111, and a database update unit 612 that are program modules executed by the processor 621. Since the relationship between the program modules stored in the processor 624 and the memory 625 and the functional blocks realized by them, and the variation of the method for realizing the functional blocks are the same as those described for the database update device 601, details The detailed explanation is omitted.

  A network I / F 626 is an interface that is connected to the network 604 and communicates with other devices via the network 604.

  The storage device 104 is a so-called auxiliary storage device such as a hard disk drive or a flash memory, and stores a camera state estimation database 105. At least a part of the information included in the camera state estimation database 105 may be copied to the memory 625 as necessary.

  The storage device 104 or the memory 625 further stores one or more parameter values indicating the camera state. At least one of these values is an estimated value of an unknown value, which is updated by processing to be described later, and is transmitted to the image recognition processing device 603 when a predetermined condition is satisfied.

  The database update unit 612 receives the database update information output from the database update device 601 and updates the camera state estimation database 105. The image acquisition unit 102 receives the camera state estimation process start signal output from the database update device 601 and acquires an image from the camera 101. The image matching processing unit 110 and the camera state estimation unit 111 estimate the camera state based on the image received from the camera 101 and the information in the camera state estimation database 105, and transmit the camera state to the image recognition processing device 603.

  The image recognition processing device 603 includes a processor 627, a memory 628, a network I / F 629, and a display device 614. The processor 627 implements various functions of the image recognition processing device 603 described below by executing a program stored in the memory 628. The memory 622 includes an image acquisition unit 616, a camera state setting unit 613, and an image recognition processing unit 615 that are program modules executed by the processor 621. Since the relationship between the program modules stored in the processor 627 and the memory 628 and the functional blocks realized by them, and variations of the method for realizing the functional blocks are the same as those described for the database update device 601, the details are as follows. The detailed explanation is omitted.

  A network I / F 629 is an interface that is connected to the network 604 and communicates with other devices via the network 604. The display device 614 is a display device for characters and images such as a liquid crystal display device.

  The image recognition processing unit 615 applies a predetermined image recognition process to the video from the camera acquired by the image acquisition unit 616. The image recognition processing performed by the image recognition processing unit 615 may be any type, and specific examples include a process for detecting a vehicle having a certain speed or more. The image recognition processing unit 615 displays the processing result on the display device 614. The camera state setting unit 613 receives the camera state output from the camera state estimation device 100 and updates the parameter value of the camera state used in the image recognition processing unit 615.

  With the configuration described above, necessary target information can be added to the camera state estimation database 105 while viewing the video displayed on the database update device 601. As a result, a database suitable for the environment in which the camera is actually displayed can be constructed. Then, by estimating the camera state based on the added information, it is possible to estimate the camera state more accurately. By using the camera state estimated in this way, even when the camera state changes, the image recognition processing unit 615 can perform image recognition processing using the estimated camera state.

  In the example of FIG. 3, the database update device 601, the camera state estimation device 100, and the image recognition processing device 603 are separate devices, but the database update device 601 and the camera state estimation device 100 may be the same device. Further, the database update apparatus 601 may be omitted by providing the image recognition processing apparatus 603 with the same function as the database update apparatus 601. Each device may be connected via one network 604, but may be connected via a plurality of networks or general cables.

  FIG. 4 is an explanatory diagram of an example of the camera state estimation database 105 according to the first embodiment of the present invention.

  In the camera state estimation database 105, information is managed for each ID (identifier) 302. For each ID, a target image 303 that is an image to be detected, a feature amount 304 of the image, and a size of the detection target are measured. The reference position coordinates 305 indicating the coordinates of the reference position and the height 306 indicating the size of the detection target are stored in association with each other.

  The target image 303 stores an image of a specific detection target used for camera state estimation. In this embodiment, a specific detection target used for camera state estimation is described as a detection target or a target. For example, an image of a moving object such as a person or a car or an image of a non-moving object such as a post box (post box) is cut out from an image captured by the camera 101 and stored as a target image 303 to be detected. be able to.

  The feature quantity 304 stores a feature quantity that is a numerical sequence derived from the color and shape of the image. This feature amount is used to compare the feature amount in the photographed image with the feature amount 304 in the database in the image matching process for determining whether or not the detection target exists in the photographed image.

  The reference position coordinate 305 stores information on the position where the size of the detection target is measured. FIG. 4 is an example in which pixel coordinate values (that is, coordinate values on the screen) of points at which the size of the detection target (height in the example of FIG. 4) is measured are stored. The height 306 stores information related to the size of the real space to be detected. In this embodiment, an example in which height information in the real space of the detection target is used as information on the size of the detection target is shown, but information on a size other than the height, such as the width of the detection target, may be used. .

  Specifically, as will be described later with reference to FIGS. 9 and 10, when an area including a detection target is cut out from an image captured by the camera 101 and stored in the target image 303, the cut-out is performed. The coordinate value on the image at the point where the size of the detection target included in the image is measured is stored in the reference position coordinate 305. For example, when the detection target is a person and the size of the detection target is the height from the lower end (foot) of the person to the top of the head, that is, the height, the cut out of the top of the person included in the cut out image Coordinate values on the image (for example, coordinate values in a two-dimensional coordinate system with the upper left corner of the image cut into a rectangle as the origin) are stored in the reference position coordinates 305.

  For example, in the initial state of the image recognition system of the present embodiment, data may not be stored in the camera state estimation database 105. In that case, the database update device 601 and the camera state estimation device 100 in the image recognition system extract the detection target from the image captured by the camera 101 and store the data related thereto in the camera state estimation database 105. When a sufficient amount of data relating to the detection target is stored in the camera state estimation database 105 by this database update process (see FIG. 9, FIG. 10, etc.), the camera state estimation device 100 uses the stored data. Thus, image matching processing and camera state estimation processing described later can be performed.

  Alternatively, in the initial state of the image recognition system, data relating to a typical detection target may already be stored in the camera state estimation database 105. In that case, the camera state estimation apparatus 100 may perform image matching processing and camera state estimation processing by continuously using those data.

  In any case, new data is added by performing the above database update process as necessary (for example, when new data is required due to a change in the installation environment of the camera 101). Can be used for subsequent processing.

  FIG. 5 is a block diagram illustrating a configuration example of the image matching processing unit 110 according to the first embodiment of this invention.

  The image matching processing unit 110 includes a feature amount matching degree calculation processing unit 403, a threshold determination processing unit 404, and a matching target rectangle extraction unit 405. These are program modules included in the image matching processing unit 110, for example.

  An image 401 from the camera is captured and transmitted by the camera 101 and is acquired by the image acquisition unit 102. The timing of image acquisition execution is determined by an image acquisition execution signal 406 input from the outside. The image acquisition execution signal 406 may be manually input from, for example, an operation terminal (not shown) connected to the network 604, or may be automatically issued at regular intervals. In addition, when the position or orientation of the camera 101 can be operated, the camera state can be estimated every time the operation is performed by inputting an operation signal as the image acquisition execution signal 406. Further, when receiving the image acquisition execution signal 406, the image acquisition unit 102 may continue to acquire images of a plurality of frames for a certain period from the signal reception and use them for processing.

  The image acquired by the image acquisition unit 102 is input to the feature amount matching degree calculation processing unit 403. Further, the camera state estimation data from the camera state estimation database 105 stored in the storage medium 402 (for example, hard disk) of the storage device 104 is input to the feature amount matching degree calculation processing unit 403. In the feature amount matching degree calculation processing unit 403, the camera state estimation database 105 is included in the image acquired by the image acquisition unit 102 using the image feature amount of each detection target data recorded in the camera state estimation database 105. It is checked whether there is a part similar to each detection target data recorded in the. The presence or absence of a similar part can be determined by, for example, a process similar to a general template matching process.

  The template matching process selects a part of the input image, extracts a feature quantity from the selected area, and compares the feature quantity with the feature quantity stored in the database. This is a process of scanning the screen by repeatedly executing while shifting, and the degree of coincidence for each partial region in the screen is calculated. The feature amount matching degree calculation processing unit 403 performs the above-described template matching processing for each detection target in the database, and calculates the matching degree for each partial region of the image with respect to the feature amount of each detection target.

  The threshold determination processing unit 404 performs a process of selecting a detection target whose matching degree calculated by the feature amount matching degree calculation processing unit 403 is higher than a certain value (threshold value). At this time, for example, when the degree of coincidence of a plurality of detection targets stored in the camera state estimation database 105 is high for the same region of the photographed image, the detection target with the highest degree of coincidence among them is detected. Data may be selected. In the following description, a detection target having a high matching degree (or a selected detection target when one of a plurality of detection targets having a high matching degree is selected) is also referred to as a “matching detection target”. On the other hand, a region on the image having a high degree of matching with the detection target is also referred to as “matching target”.

  In addition, since the feature quantity of the area of the captured image and the feature quantity of the detection target stored in the database are almost completely inconsistent, the above-mentioned degree of coincidence can be rephrased as similarity, The “matched detection target” can be paraphrased as “similar detection target” or “detection target with a high similarity (above a predetermined threshold)”, for example.

  The matching target rectangle extraction unit 405 receives the information on the detection target selected by the threshold determination processing unit 404 (that is, the matching detection target) and the position information of the area on the screen in which the detection target is shown. . The matching target rectangle extraction unit 405 determines which range in the input image the matched detection target matches, and outputs information for specifying the range as matching target rectangle information. For example, the matching target rectangle extraction unit 405 determines the position of the rectangular area obtained by scaling the size of the rectangular area determined to have a high degree of matching with the size of the image stored in the matching detection target database. And information for identifying the above and the enlargement ratio at the time of scaling described above are output. Here, the information specifying the position of the rectangular area may include, for example, the coordinate value of the upper left corner of the rectangular area (hereinafter also referred to as the upper left coordinate) in the coordinate system on the image including the rectangular area. .

  As described above, the image matching processing unit 110 is based on the information in the database stored in the storage medium 402, and whether the detection target is reflected in the photographed image (that is, the detection target and characteristics stored in the database). Whether there is a region including a target having a similar amount) is extracted, and if it is reflected, the region is extracted, and the extracted region information and the detection target information stored in the database are output in association with each other.

  Note that the high degree of coincidence between the feature quantity of a certain area in the photographed image and the feature quantity of the detection target stored in the camera state estimation database 105 indicates that the detection target is reflected in the area (at least It is likely). However, this does not necessarily mean that the detection target and the detection target reflected in the area are the same object (for example, the same person), and are the same type of object (for example, different persons). It means that both are humans). By adjusting the feature value calculation method and the threshold for determining the degree of coincidence, it is possible to make it difficult to detect other than the same object, or to easily detect the same kind of object.

  When the same kind of object is detected, but not the same, the actual size may be different from the size stored in the camera state estimation database 105, but the same kind of object having a high degree of matching of the feature amount. Since the individual difference in height (height difference for humans) is not so large, the size information (for example, height 306) stored in the camera state estimation database 105 is used for camera state estimation described later. Even if it is used in processing, the camera state can be estimated with sufficient accuracy.

  Even if you want to detect the same kind of objects, if the type includes multiple categories with different appearance characteristics and sizes (for example, adults and children, small passenger cars and large trucks, etc.) The feature amount and size of each category of object may be stored in the camera state estimation database 105. On the other hand, when it is desired to detect only one object, such as a building or a signboard exemplified in Example 3 described later, feature amounts and threshold values suitable for detecting the same object are used.

  FIG. 6 is a block diagram illustrating a configuration example of the camera state estimation unit 111 according to the first embodiment of the present invention.

  The camera state estimation unit 111 includes a target reference point pixel calculation unit 505, a target lower end pixel calculation unit 506, a target foot coordinate calculation unit 508, a target lower end height calculation unit 509, a target lower end error calculation unit 510, and a camera state update processing unit 511. And a camera state output determination unit 512. These are, for example, program modules included in the camera state estimation unit 111.

  A processing result 501 of the image matching processing unit 110 is input to the camera state estimation unit 111. The processing result 501 of the image matching processing unit includes the same number of sets of target height 502, reference position coordinates 503, and matching target rectangle information 504 as the number of matching targets extracted. Among the processing results 501 regarding each detection target, the target height 502 is the height 306 corresponding to the target image 303 of the matching detection target, and the reference position coordinate 503 is the reference position coordinate 305 corresponding to the target image 303. The matching target rectangle information 504 includes information for specifying the position of the matching target rectangular area output from the matching target rectangle extraction unit 405 and the enlargement ratio at the time of scaling.

  In this embodiment, when two or more matching targets are extracted (that is, when two or more sets of the target height 502 to the matching target rectangle information 504 are input), the camera state estimation unit 111 displays the figure. 6. The camera state estimation process shown in FIG. 6 is performed, but if the number of matching targets is less than two, the camera state cannot be estimated correctly, so the process is not performed and processing is performed until information on two or more matching targets is input. Wait. At this time, if the camera state does not change over a plurality of frames, the matching included in the image matching processing result for the plurality of frames (that is, a plurality of images taken at different times during a period in which the camera state does not change) Camera state estimation processing may be performed using all target information.

  Among the pieces of input information, the reference position coordinates 503 and the matching target rectangle information 504 are input to the target reference point pixel calculation unit 505. The target reference point pixel calculation unit 505 calculates the coordinates of the reference point pixel to be detected in the screen from the input reference position coordinates 503 and the matching target rectangle information 504. In this process, the target reference point pixel calculation unit 505 extracts the upper left coordinates and enlargement ratio of the rectangle from the matching target rectangle information 504, and adds the value of the reference position coordinates multiplied by the enlargement ratio to the upper left coordinates of the rectangle. Thus, the coordinates of the target reference point pixel can be calculated. The calculated coordinates of the reference point pixel (that is, the target reference point pixel) are input to the target foot coordinate calculation unit 508.

  The target foot coordinate calculation unit 508 receives the camera state 507, the coordinates calculated by the target reference point pixel calculation unit 505, and the target height 502. Here, the camera state 507 means the height of the camera, the angle with respect to the ground, and the angle of view. In the camera state 507, for example, a parameter value set in advance is set as an initial state, and then updated by processing to be described later. Normally, the camera state 507 does not match the actual state of the camera 101 at the time when the image input from the image acquisition unit 102 is captured in the initial state, and is updated by a process described later. Close to the actual state.

  The target foot coordinate calculation unit 508 calculates the foot coordinates 211 of the detection target using the coordinates of the target reference point pixel and the target height 502. The foot coordinates are calculated using a transformation matrix that transforms camera coordinates (that is, coordinates on an image captured by the camera 101) based on a generally known perspective projection model and real space coordinates.

  Here, the position of the ground directly below the camera is set as the origin 210, the X axis 207 and Y axis 208 of real space coordinates are set on the ground plane, and the Z axis 209 of real space coordinates is set in a direction perpendicular to the ground. In this case, if the number of pixels of the camera 101, the angle of view, the distance between the ground and the camera, and the angle of the camera with respect to the ground are specified based on the camera state 507 or the like, the coordinates of the points on the real space coordinates are changed to Obviously, it can be easily converted to coordinates. By using this relationship and setting the value of the Z coordinate on the real space coordinate to the target height 502, the reference position coordinate 503 can be converted to the real space coordinate. Based on the camera state 307 and the position of the target included in the input image in the image, the direction from the installation position of the camera 101 to the target in real space can be calculated. Further, the camera state 307 The distance in the real space from the camera 101 to the object can be calculated based on the reference position coordinates in the image of the object included in the input image and the object height 502 indicating the size of the object in the actual space. Because.

  The target foot coordinate calculation unit 508 is a point on the ground just below the real space coordinate point of the reference position coordinate 503 converted in this way (for example, when a person is extracted as the target, the lower end of the target person) That is, the coordinate value of the foot) is output as the target foot coordinate 211. Although an example of the calculation method is shown here, the target foot coordinates 211 may be calculated using other calculation methods. The calculated foot coordinates 211 of the target are input to the target bottom height calculation unit 509.

  The matching target rectangle information 504 is input to the target bottom pixel calculation unit 506. The target lower end pixel calculation unit 506 calculates the lower end pixel to be matched from the input match target rectangle information 504. Here, out of the upper left coordinates of the rectangle of the matching target rectangle information 504, a coordinate value obtained by adding a value that is half the width of the rectangle to the X coordinate and adding a value of the height of the rectangle to the Y coordinate is used as the lower end of the target. Calculate as a pixel. Here, the width and height of the rectangle may be included in the matching target rectangle information 504, or the size of the target image 303 that matches the detection target in the input image and the matching target rectangle information 504. May be calculated based on the enlargement ratio. The calculated target lower end pixel is input to the target lower end height calculation unit 509.

  The target bottom edge height calculation unit 509 receives the target bottom pixel, the target foot coordinates, and the camera state 507. Here, similarly to the target foot coordinate calculation unit 508, calculation using a transformation matrix for converting camera coordinates and real space coordinates based on the perspective projection model of the camera is performed. First, when the target lower end height calculation unit 509 converts the target lower end pixel into a coordinate value on real space coordinates (this is also referred to as target lower end coordinate), the converted X coordinate and Y coordinate of the target lower end coordinate are converted. Assume that (that is, coordinates in the horizontal plane) match the X and Y coordinates of the target foot coordinates. By using this assumption, the Z coordinate of the real space coordinates of the target lower end coordinates can be calculated using a perspective projection model. The target lower end height calculation unit 509 outputs, as the target lower end height, the difference between the calculated value of the Z coordinate of the real space coordinate of the target lower end coordinate and the value of the Z coordinate of the real space coordinate of the target foot coordinate. . The output target lower end height is input to the target lower end error calculation unit 510.

  The target lower end error calculation unit 510 calculates an error of the target lower end height from the target lower end height calculated for all the detection targets. When the camera state 507 is a correct value, the target bottom height obtained through the processing of the target foot coordinate calculation unit 508 and the processing of the target bottom height calculation unit 509 is ideally zero. That is, it can be said that the camera state 507 has a correct value when the target lower end height is 0 for all detection targets.

  On the other hand, when the camera state 507 is not a correct value, the target lower end height does not become 0 for all detection targets. For example, when the angle of view included in the camera state 507 used for the calculation is narrower than the actual angle of view of the camera 101, the target foot coordinate calculation unit 508 is based on the size of the detection target on the captured image. The distance from the camera 101 to the detection target is mistakenly smaller than the actual distance. As a result, since the X and Y coordinates of the target foot coordinates are closer to the camera 101 than actual (that is, closer to the origin 210), the real space coordinates of the target bottom pixel calculated by the target bottom height calculation unit 509 are obtained. The upper position is also close to the camera 101. That is, as shown in FIG. 2, when the camera 101 captures an image of the detection target, the target bottom height calculated by the target bottom height calculation unit 509 is larger than 0 (that is, from the ground 106). Higher).

  For example, when a plurality of parameters included in the camera state 507 are unknown, such as the depression angle and the angle of view of the camera 101, the target lower end height is set for any detection target even though none of them is correct. Can be zero. However, in this case, since the target lower end height does not become 0 for another detection target (with a different distance from the camera 101), the target lower end height does not become 0 for all detection targets. Thus, it can be detected that the camera state includes an error.

  The target lower end error calculation unit 510 sums the target lower end heights calculated for all the detection targets, and outputs the total value to the camera state output determination unit 512 as an error value of the target lower end height. Specifically, for example, the sum or the sum of squares of the absolute values of the difference between the Z coordinate value of the real space coordinate of the target bottom coordinate and the Z coordinate value of the real space coordinate of the target foot coordinate is calculated. Good. This total value corresponds to an index indicating the magnitude of the error in the camera state 507.

  The camera state output determination unit 512 determines whether to output the camera state based on the error value input from the target lower end error calculation unit 510. The conditions for outputting the camera state 507 include, for example, that the error value has become a certain value or less, or that the change from the error value at the previous determination is small. If such a condition is satisfied, the camera state 507 set at that time is sufficiently close to the actual state of the camera 101, or it is difficult to make it closer to the actual state. Since it is considered, it is determined that the camera state 507 is output. When it is determined that the camera state is not output, the camera state output determination unit 512 outputs the input error value to the camera state update processing unit 511.

  The camera state update processing unit 511 updates the current camera state 507. As an update method, for example, when the error value input this time is observed and the error value is smaller than the error value input last time, the change amount of the same amount as the parameter change value at the previous camera state update is displayed. When the error value input this time is larger than the error value input last time, the sign of the parameter change at the previous camera state update is reversed. There is a method of outputting a value obtained by adding a half value to the current parameter as a new camera state 507.

  As described above, in the present embodiment, the camera state that finally reduces the error value by repeatedly executing the processing in the series of processing units from the target lower end height calculation unit 509 to the camera state update processing unit 511. 507 is calculated and output. By this series of processing, all the detection targets are grounded on the ground coordinates obtained from the estimated camera state 507, and the correct camera state 507 can be estimated. In this embodiment, an example is shown in which the camera state is updated based on the error amount during the iterative processing. However, the target bottom end error is calculated for all the randomly prepared camera state patterns, and the error is the smallest. A method such as selecting a camera state can also be used.

  FIG. 7 is a flowchart of image matching processing according to the first embodiment of the present invention.

  In the image matching processing, the image matching processing unit 110 matches an image captured by the camera 101 with detection target information in the camera state estimation database 105, detects a detection target included in the image, and detects target information. It is a process to acquire. Details of processing executed by each unit in the image matching processing unit 110 in each step of FIG. 7 are as described with reference to FIG.

  First, the feature amount matching degree calculation processing unit 403 acquires detection target information from the camera state estimation database 105 (S801).

  Next, the feature amount coincidence calculation processing unit 403 performs the processes of S803, S804, and S805 on all the acquired detection target information (S802). In the image partial area designation processing step (S803), the feature amount coincidence calculation processing unit 403 designates a partial area for calculating the feature amount coincidence from the image captured by the camera 101. Next, the feature amount coincidence calculation processing unit 403 executes a feature amount coincidence degree calculation process (S805) for all the specified partial areas (S804). The feature amount matching degree calculation processing unit 403 compares the image of each partial region with the detection target information in the camera state estimation database 105, and calculates the degree of matching (S805).

  The threshold determination processing unit 404 executes threshold determination processing for all the calculated degrees of coincidence (S806). Next, the threshold determination processing unit 404 determines whether there is a matching detection target (S807). If there is a matching detection target (S807: Yes), the matching target rectangle extraction unit 405 performs S808, S809. Then, the process of S810 is performed, and if there is no coincident detection target (S807: No), the process ends.

  In S808, the matching target rectangle extraction unit 405 executes a process of extracting matching target rectangles (S809) for all the matching detection targets. When all the matching target rectangles are extracted, the matching target rectangle extraction unit 405 outputs matching target information (S810), and completes the image matching process.

  FIG. 8 is a flowchart of the camera state estimation process according to the first embodiment of the present invention.

  Details of processing executed by each unit in the camera state estimation unit 111 in each step of FIG. 8 are as described with reference to FIG.

  First, the camera state estimation unit 111 determines whether or not two or more matching targets are input (S901). If two or more matching targets are not input (S901: No), the next input is performed. The system waits (S902), and when two or more matching targets are input (S901: Yes), the processing from S903 is executed. Here, the two or more matching targets may be matching targets similar to other detection targets stored in the camera state estimation database 105, or may be two or more similar to the same detection target. May be a matching target.

  Next, the camera state estimation unit 111 sets an initial value of the camera state (S903).

  Next, the camera state estimation unit 111 performs the processing from S905 to S908 on all input matching target information (S904). The target reference point pixel calculation unit 505 executes a detection target reference point pixel calculation process using the input detection target information (S905). The target lower pixel calculation unit 506 executes the calculation process of the lower pixel of the detection target using the input detection target information (S906). The target foot coordinate calculation unit 508 calculates the foot coordinates of the detection target using the set camera state, the input target reference point pixel, and the target height (S907). The target lower end height calculation unit 509 calculates the lower end height of the target using the input foot coordinates of the detection target and the target lower end pixel (S908).

  When the processing from S905 to S908 is completed for all the matching target information, the target lower end error calculation unit 510 next calculates the target lower end error using the target lower end height obtained from all the matching target information. (S909). Next, the camera state output determination unit 512 determines whether to output the current camera state based on the calculated target bottom end error (S910). When it is determined that the camera state is not output (S910: No), the camera state update processing unit 511 updates the camera state (S911), and then the camera state estimation unit 111 repeats the processing from S904 to S909. When it is determined in S910 that the camera state is output (S910: Yes), the camera state estimation unit 111 outputs the camera state (S912), and the camera state estimation process is completed.

  As illustrated in FIG. 8, the camera state estimation unit 111 executes the processing after S904 when a plurality of matching targets are input. This is because when the two or more independent parameters included in the camera state 507 are unknown, such as the depression angle and the angle of view of the camera 101, the target bottom height is sufficiently high for two or more matching targets. This is because it is necessary to search for a combination of parameters to be reduced. Therefore, when there is only one unknown parameter (for example, when the installation position and direction of the camera 101 are fixed and the angle is already obtained, and only the angle of view is variable by the zoom function). By repeatedly executing S905 to S911 for one matching target, the value of the parameter can be specified. In this case, the target lower end height calculated for one matching target is used as an index indicating the magnitude of the error in the camera state.

  FIG. 9 is a flowchart of the state estimation database update process according to the first embodiment of the present invention.

  First, the database information setting unit 609 executes processing for selecting an image to be detected to be added to the camera state estimation database 105 (S1001). Next, the database information setting unit 609 executes processing for inputting information on the size (for example, height) of the detection target to be added to the camera state estimation database 105 (S1002). Next, the database information setting unit 609 executes processing for designating information on a reference point obtained by measuring the size (for example, height) of the detection target (S1003). A specific example of the processing from S1001 to S1003 will be described later with reference to FIG.

  Next, the database information setting unit 609 executes processing for adding the information input in S1001 to S1003 to the camera state estimation database 105 (S1004). Specifically, for example, the database information setting unit 609 transmits the information input in S1001 to S1003 to the camera state estimation apparatus 100 via the network I / F 623 and the network 604, and the database of the camera state estimation apparatus 100 The update unit 612 adds the information to the camera state estimation database 105 in the storage device 104.

  Next, the database update unit 612 determines whether or not a sufficient amount of data has been added to the camera state estimation database 105 (S1005). When the amount of additional data is insufficient (S1005: No), the database update unit 612 repeats the processing from S1001 to S1004 again. If the amount of additional data is sufficient (S1005: Yes), the state estimation database update process is completed.

  The determination of whether or not the amount of additional data is sufficient can be made based on a criterion according to the installation environment of the camera 101 or the like. For example, the database update unit 612 determines that a sufficient amount of data has been added when the number of added detection targets or the number of detection target types (for example, people, cars, etc.) exceeds a predetermined threshold. May be.

  Specifically, for example, when the camera 101 is installed on a road where there are many people but a car does not travel, the amount of additional data is sufficient when data on a predetermined number of people is added. May be determined. In an environment where many people are always photographed, if the data of one person is stored in the database, the camera state may be estimated. In an environment where both a person and a vehicle can be photographed, it may be determined that the amount of additional data is sufficient when data for a predetermined number of persons and vehicles are added.

  Alternatively, for example, when the camera 101 is installed at a high place and a wide range is monitored, it is assumed that a detection target fixed like a signboard or a building is mainly added. In such a case, by adding data, a predetermined number of detection targets are always included in the image regardless of the angle of view and direction of the camera 101 within the movable range. Sometimes it may be determined that a sufficient amount of data has been added.

  Alternatively, the user may determine whether a sufficient amount of data has been added and input the result to the database update device 601. In this case, the input determination result may be transmitted from the database update device 601 to the camera state estimation device 100, and the database update unit 612 may make the determination in S1005 based on the result.

  FIG. 10 is an explanatory diagram illustrating an example of display in the database update device 601 according to the first embodiment of this invention.

  The display device 701 in FIG. 10 corresponds to the display device 608 in FIG. 3, and the character input terminal 702 (for example, keyboard) and the cursor operation terminal 703 (for example, mouse) in FIG. 10 correspond to the input terminal 607 in FIG. . A character input terminal 702 and a cursor operation terminal 703 are connected to the display device 701. On the screen of the display device 701, a camera image 704, a data information display field 705 for a camera state estimation database, a height input field 706, a database addition process execution button 707, a camera state estimation start button 708, and a cursor 709 are displayed. And an area 710 obtained as a result of the cursor operation are displayed. A camera image 704 is an image captured by the camera 101 and is acquired by the image acquisition unit 617.

  Height information input from the character input terminal 702 is displayed in the height input field 706. The cursor 709 is operated by the cursor operation terminal 703. In the addition target image selection process S1001, the user can select a part of the area 710 on the image including the detection target (post box in the example of FIG. 10) to which data is to be added by a cursor operation. . Further, the user inputs the height information of the detection target of the selected area in the height input field 706 in the additional target height input process S1002. Further, in the target reference point information specifying process S1003, the user specifies the reference point 711 to be detected in the selected area by operating the cursor. For example, when the detection target is a mail box, a pixel on the upper surface thereof is designated as the reference point 711, and when the detection target is a person, a pixel at the top of the head is designated as the reference point 711.

  In the database addition process S1004, when the user operates the database addition process execution button 707 using the cursor operation terminal 703, the database information setting unit 609 causes the image data of the area 710 selected using the cursor 709, The height information input in the input field 706 and the coordinate value on the image of the designated reference point 711 are transmitted to the camera state estimation apparatus 100.

  The database update unit 612 of the camera state estimation apparatus 100 that has received these pieces of information uses the image data of the area 710 received in the target image 303 of the new record in the camera state estimation database 105 as a feature amount based on the received information. The camera state estimation database 105 is updated by adding the feature amount calculated from the image data to 304, the coordinates of the reference point 711 received to the reference position coordinates 305, and the height information to the height 306, respectively. To do.

  After the camera state estimation database 105 is updated, the database information acquisition unit 611 displays the updated result in the data information display field 705. When the user operates the camera state estimation start button 708, the camera state estimation signal output unit 610 generates a signal for starting the camera state estimation process and transmits the signal to the camera state estimation apparatus 100. A camera state estimation process is executed by the camera state estimation apparatus 100 that has received the signal.

  In the example of FIG. 10, a mouse and a keyboard are assumed as the input terminal 607, but a touch panel or the like may be used. In addition, although the display device 701 in FIG. 10 displays one camera image, images from a plurality of cameras may be displayed simultaneously. The display device 701 may display a field for designating a record to be deleted from the camera state estimation database 105.

  According to the first embodiment described above, a database in which information related to the size of a detection target touching the ground is associated with an image is prepared, and detection is performed by detecting the detection target existing in the camera image. The state of the camera can be estimated from the detected size information of the detection target. In addition, the database can be easily updated by configuring the database with simple information such as the image to be detected and the size information. As a result, if the detection target registered in the database is reflected in the image, the camera state can be estimated one by one. In addition, since the database can be updated as necessary, the camera state can be estimated more accurately by updating the database according to the environment in which the camera is projected.

  Next, Embodiment 2 of the present invention will be described with reference to FIG. Except for differences described below, each part of the image recognition system according to the second embodiment has the same function as each part denoted by the same reference numeral in the first embodiment shown in FIGS. Description of is omitted.

  In this embodiment, the camera 101 includes a camera state estimation database 105, an image matching processing unit 110, and a camera state estimation unit 111.

  FIG. 11 is a block diagram illustrating a hardware configuration of the image recognition system according to the second embodiment of the present invention.

  11, the camera state estimation database 105, the image matching processing unit 110, the camera state estimation unit 111, the input terminal 607, the display device 608, and the database information illustrated in FIG. 3 in the configuration illustrated in FIG. The same components as the setting unit 609, the camera state estimation signal output unit 610, the database information acquisition unit 611, the database update unit 612, the camera state setting unit 613, the image recognition processing unit 615, and the image acquisition unit 617 are assigned the same reference numerals. The description is omitted.

  That is, the camera 101 of the present embodiment has functions equivalent to those of the camera state estimation device 100 and functions of portions other than the display device 614 of the image recognition processing device 603 in addition to the camera 101 of the first embodiment shown in FIG. It can be said that the camera state estimation apparatus 100 according to the first embodiment illustrated in FIG. 3 has functions equivalent to those of the camera 101 according to the first embodiment and functions other than the display apparatus 614 of the image recognition processing apparatus 603. It can also be said that these are added.

  The camera 101 according to the present exemplary embodiment includes a processor 1102, a memory 1103, a network I / F 1104, an image photographing unit 1101, and a storage device 1105.

  The processor 1102 implements various functions of the camera 101 according to the present exemplary embodiment by executing a program stored in the memory 1103.

  The memory 1103 includes an image matching processing unit 110, a camera state estimation unit 111, a camera state estimation signal output unit 610, a database update unit 612, a camera state setting unit 613, and an image recognition processing unit, which are program modules executed by the processor 1102. 615 is stored. As in the first embodiment, each of these units is handled as a functional block, but in actuality, the processing of each unit is executed by the processor 1102 according to instructions described in each program module. As in the first embodiment, any part or all of the functional blocks may be realized by dedicated hardware.

  A network I / F 1104 is an interface that is connected to the network 604 and communicates with the database update device 601 and the like via the network 604.

  The image capturing unit 1101 has an optical system including a lens and an image sensor, and performs image capturing using them. The camera 101 according to the present embodiment estimates the camera state necessary for the processing of the image recognition processing unit 615 using the image captured by the image capturing unit 1101 and the information stored in the camera state estimation database 105. Can do.

  The storage device 1105 is a so-called auxiliary storage device such as a hard disk drive or a flash memory, and stores the camera state estimation database 105. The database update unit 612 updates the camera state estimation database 105 using information input from the outside (for example, from the database update device via the network 604).

  Similar to the database update device 601 (FIG. 3) of the first embodiment, the database update device 601 of the present embodiment includes a processor 621, a memory 622, a network I / F 623, an input terminal 607, and a display device 608. The memory 622 stores a database information setting unit 609, a database information acquisition unit 611, an image acquisition unit 617, and an image recognition result acquisition unit 1106, which are program modules executed by the processor 621. The image recognition result acquisition unit 1106 acquires the image recognition result processed by the camera 101 and displays it on the display device 608. The database update device 601 of this embodiment includes a database update function and a camera video image recognition result display function. However, these functions may be divided into separate devices.

  As described above, in the present embodiment, the camera state estimation apparatus 100 according to the first embodiment performs by providing the camera state estimation database 105, the image matching processing unit 110, and the camera state estimation unit 111 in the camera 101. The same processing as that described above can be executed inside the camera 101. As a result, even when the number of cameras 101 is increased, the camera state estimation process can be performed by each camera 101. Therefore, the calculation load can be reduced by distributing the load on the entire system to each camera 101. Is possible.

  Next, Embodiment 3 of the present invention will be described with reference to FIG. Except for the differences described below, each part of the image recognition system according to the second embodiment has the same function as each part according to the first embodiment shown in FIGS. 1 to 10 or the second embodiment shown in FIG. Therefore, illustration and description thereof are omitted.

  Specifically, the image recognition system according to the third embodiment has a camera state estimation database 1201 instead of (or in addition to) the camera state estimation database 105, and performs processing using the same. This is the same as the image recognition system of the first or second embodiment.

  FIG. 12 is an explanatory diagram of an example of the camera state estimation database 1201 according to the third embodiment of the present invention.

  The camera state estimation database 1201 shown in FIG. 12 is in addition to the target ID 302, target image 303, feature amount 304, reference position 305, and height 306 held in the camera state estimation database 105 shown in FIG. The object width 1202 and the object position information (for example, latitude and longitude) 1203 are held.

  The target foot coordinate calculation unit 508 refers to the camera state estimation database 1201 shown in FIG. 12 and uses not only the height 306 but also the width 1202 when the detection target is detected from within the image. It is possible to calculate the foot coordinates of the detection target (S907). The camera state estimation unit 111 can estimate the direction in which the camera 101 is facing by referring to the position information 1203 of the detection target. For example, when it can be detected that a detection target with ID 302 of “1” exists in the image, a point specified by latitude and longitude (35, 139) registered as position information 1203 in the field of view of the camera It can be seen that the direction is included. In order to specify this direction, the camera state estimation unit 111 may convert the latitude and longitude values into coordinate values in the coordinate system of the real space shown in FIG. 2, or from the beginning of the real space shown in FIG. The coordinate value may be stored in the camera state estimation database 1201 as the position information 1203.

  As described above, in this embodiment, the camera state estimation database 1201 includes information on a plurality of types of sizes, and further includes information on the position. As a result, the camera state can be estimated with higher accuracy, and the direction in which the camera is facing can also be estimated.

  In addition, this invention is not limited to the Example mentioned above, Various modifications are included. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of a certain embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of a certain embodiment. Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.

  Each of the above-described configurations, functions, processing units, processing means, and the like may be realized by hardware by designing a part or all of them with, for example, an integrated circuit. Further, each of the above-described configurations, functions, and the like may be realized by software by interpreting and executing a program that realizes each function by the processor. Information such as programs, tables, and files for realizing each function is a memory, a hard disk drive, a storage device such as an SSD (Solid State Drive), or a computer-readable non-transitory data such as an IC card, an SD card, or a DVD. It can be stored in a storage medium.

  Further, the drawings show control lines and information lines that are considered necessary for explaining the embodiments, and not necessarily all control lines and information lines included in an actual product to which the present invention is applied. Not necessarily. Actually, it may be considered that almost all the components are connected to each other.

Claims (15)

An image recognition system comprising a camera and a camera state estimation device connected to the camera via a network,
The camera state estimation device includes a first interface connected to the network, a first processor connected to the first interface, and a first storage device connected to the first processor,
The first storage device holds a feature amount of an image to be detected, a size of the detection target in real space, and an estimated value of one or more parameters indicating a state of the camera,
The camera captures an image and transmits it to the camera state estimation device,
The first processor is
Extracting a target similar to the detection target based on the feature amount held in the first storage device from the image captured by the camera,
An error in the estimated value of the one or more parameters based on the size of the detection target in real space, the estimated value of the one or more parameters, and the position and size of the similar target in the image. Calculate an indicator of the size of
An image recognition system, wherein an estimated value of the one or more parameters is changed when an index indicating the calculated error size does not satisfy a predetermined condition. The image recognition system according to claim 1,
The size of the detection target in real space includes a height from a lower end of the detection target to a predetermined reference point of the detection target;
The first processor is
The size of the detection target in real space, the estimated value of the one or more parameters, the position of the reference point of the similar target in the image, and the size of the similar target in the image. Based on the first coordinate value in the real space at the lower end of the similar object,
The similar target based on the calculated coordinate value in the horizontal plane among the first coordinate values, the estimated value of the one or more parameters, and the position of the lower end of the similar target in the image. Calculate the second coordinate value in real space at the bottom of
The difference between the first coordinate value and the second coordinate value is calculated as an index indicating the magnitude of the error,
An image recognition system, wherein, when the calculated difference is smaller than a predetermined value, it is determined that the index indicating the magnitude of the error satisfies a predetermined condition. The image recognition system according to claim 2,
The first storage device holds estimated values of a plurality of parameters indicating the state of the camera,
The first processor is
Extracting a plurality of objects similar to the detection object from the image taken by the camera,
Calculating the first coordinate value in the real space of each of the plurality of similar objects;
Calculating the second coordinate value in real space at the lower end of each of the plurality of similar objects;
For each of the plurality of similar objects, the sum of differences between the first coordinate value and the second coordinate value is calculated as an index indicating the magnitude of the error,
An image recognition system according to claim 1, wherein when the calculated difference sum is smaller than a predetermined value, the index indicating the magnitude of the error is determined to satisfy a predetermined condition. The image recognition system according to claim 1,
The estimated value of the one or more parameters includes at least one of an estimated value of an angle of view of the camera and an estimated value of an angle indicating a shooting direction of the camera. The image recognition system according to claim 1,
The size in the real space of the detection target includes at least one of a height and a width in the real space of the detection target. The image recognition system according to claim 1,
The first storage device further holds coordinate values in the real space to be detected,
The estimated value of the one or more parameters includes an estimated value of an angle indicating a shooting direction of the camera,
The first processor is
An image recognition system, wherein an angle indicating a shooting direction of the camera is calculated based on the held coordinate value and the position of the similar target in the image. The image recognition system according to claim 1,
A database update device connected to the network;
The database update device is connected to a second interface connected to the network, a second processor connected to the second interface, a second storage device connected to the second processor, and the second processor. A display device, and an input device connected to the second processor,
The display device displays an image captured by the camera;
The input device receives an input of information specifying a region including a detection target and a size of the detection target in the displayed image,
The second processor transmits the input information to the camera state estimation device via the second interface,
The first processor adds the feature amount of the designated detection target image and the size of the detection target to the first storage device based on information received from the database update device. Image recognition system. A camera state estimation device having a processor and a storage device connected to the processor,
The storage device holds a feature amount of an image to be detected, a size of the detection target in real space, and an estimated value of one or more parameters indicating a camera state,
The processor is
From the image taken by the camera, based on the feature amount held in the storage device, extract a target similar to the detection target,
An error in the estimated value of the one or more parameters based on the size of the detection target in real space, the estimated value of the one or more parameters, and the position and size of the similar target in the image. Calculate an indicator of the size of
If the index indicating the magnitude of the error does not satisfy a predetermined condition, change the estimated value of the one or more parameters,
An apparatus for estimating a camera state, wherein an estimated value of the one or more parameters is output when an index indicating the magnitude of the error satisfies a predetermined condition. The camera state estimation device according to claim 8,
The size of the detection target in real space includes a height from a lower end of the detection target to a predetermined reference point of the detection target;
The processor is
The size of the detection target in real space, the estimated value of the one or more parameters, the position of the reference point of the similar target in the image, and the size of the similar target in the image. Based on the first coordinate value in the real space at the lower end of the similar object,
The similar target based on the calculated coordinate value in the horizontal plane among the first coordinate values, the estimated value of the one or more parameters, and the position of the lower end of the similar target in the image. Calculate the second coordinate value in real space at the bottom of
The difference between the first coordinate value and the second coordinate value is calculated as an index indicating the magnitude of the error,
An apparatus for estimating a camera state, wherein, when the calculated difference is smaller than a predetermined value, it is determined that an index indicating the magnitude of the error satisfies a predetermined condition. The camera state estimation apparatus according to claim 9,
The storage device holds estimated values of a plurality of parameters indicating the state of the camera,
The processor is
Extracting a plurality of objects similar to the detection object from the image taken by the camera,
Calculating the first coordinate value in the real space of each of the plurality of similar objects;
Calculating the second coordinate value in real space at the lower end of each of the plurality of similar objects;
For each of the plurality of similar objects, the sum of differences between the first coordinate value and the second coordinate value is calculated as an index indicating the magnitude of the error,
An apparatus for estimating a camera state, wherein an index indicating the magnitude of an error is determined to satisfy a predetermined condition when the calculated total difference is smaller than a predetermined value. The camera state estimation device according to claim 8,
The estimated value of the one or more parameters includes at least one of an estimated value of an angle of view of the camera and an estimated value of an angle indicating a shooting direction of the camera. The camera state estimation device according to claim 8,
The size of the detection target in the real space includes at least one of a height and a width in the real space of the detection target. The camera state estimation device according to claim 8,
The storage device further holds coordinate values in the real space of the detection target,
The estimated value of the one or more parameters includes an estimated value of an angle indicating a shooting direction of the camera,
The processor is
An apparatus for estimating a camera state, wherein an angle indicating a shooting direction of the camera is calculated based on the held coordinate value and a position of the similar target in the image. The camera state estimation device according to claim 8,
A camera state estimation apparatus further comprising the camera. A non-transitory computer-readable storage medium storing a program for controlling a computer,
The computer includes a processor and a storage device connected to the processor,
The storage device holds a feature amount of an image to be detected, a size of the detection target in real space, and an estimated value of one or more parameters indicating a camera state,
The program is
A procedure for extracting an object similar to the detection object from an image photographed by the camera based on a feature amount held in the storage device;
An error in the estimated value of the one or more parameters based on the size of the detection target in real space, the estimated value of the one or more parameters, and the position and size of the similar target in the image. A procedure for calculating an indicator of the size of
A procedure for changing an estimated value of the one or more parameters when an index indicating the magnitude of the error does not satisfy a predetermined condition;
A non-transitory computer-readable program that causes the processor to execute a procedure for outputting an estimated value of the one or more parameters when the index indicating the magnitude of the error satisfies a predetermined condition Storage medium.

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