WO2019043879A1 - Image analysis distance information provision system, image analysis distance information provision method, and program - Google Patents

Image analysis distance information provision system, image analysis distance information provision method, and program Download PDF

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WO2019043879A1
WO2019043879A1 PCT/JP2017/031381 JP2017031381W WO2019043879A1 WO 2019043879 A1 WO2019043879 A1 WO 2019043879A1 JP 2017031381 W JP2017031381 W JP 2017031381W WO 2019043879 A1 WO2019043879 A1 WO 2019043879A1
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product
image
camera
distance
analysis
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French (fr)
Japanese (ja)
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俊二 菅谷
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株式会社オプティム
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes

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  • the present invention relates to an image analysis distance information providing system, an image analysis distance information providing method, and a program for analyzing a product image taken by a camera and estimating and providing a distance between the product and the camera.
  • Patent Document 1 an image processing apparatus for processing a thermal image obtained from a two-dimensional infrared sensor and detecting highly accurate information of a person or the like in a detection area.
  • the present invention analyzes an image of a product taken by a camera, compares the size of the specified product with the number of pixels, and estimates and provides the distance between the product and the camera. It is an object of the present invention to provide an image analysis distance information providing system, an image analysis distance information providing method, and a program.
  • the present invention provides the following solutions.
  • the invention specifies the type of the product based on an image acquisition unit that acquires an image of a product taken by a camera, an image analysis unit that analyzes the image, and a result of the analysis.
  • the web content is referred to based on the type specifying means, the pixel number specifying means for specifying the number of pixels of the product shown in the image based on the analysis result, and the type of the specified product.
  • Distance estimation means for estimating the distance between the product and the camera by comparing the size acquisition means for acquiring the size of the product, the number of pixels of the product shown in the image, and the size of the product And a providing means for providing information on the estimated distance.
  • the invention specifies the type of the product based on an image acquisition step of acquiring an image of a product taken by a camera, an image analysis step of analyzing the image, and a result of the analysis.
  • the pixel number identification step for identifying the number of pixels of the product appearing in the image based on the result of the analysis, and the web content based on the type of the identified product
  • a distance obtaining step for obtaining a size of a product
  • a distance estimating step for estimating a distance between the product and the camera by comparing the number of pixels of the product shown in the image with the size of the product And providing the information of the estimated distance.
  • the invention according to the first aspect is characterized in that, on a computer, an image acquisition step of acquiring an image of a product taken by a camera, an image analysis step of analyzing the image, and a type of the product based on a result of the analysis.
  • the web content is referred to based on the type identification step of identifying the number of pixels of the product shown in the image based on the analysis result and the type of the identified product.
  • Estimate the distance between the product and the camera by comparing the size of the product with the size of the product in the image acquisition step of acquiring the size of the product, the number of pixels of the product in the image, and the size of the product
  • a program is provided for causing a distance estimation step and a provision step of providing information on the estimated distance.
  • the distance between the product and the camera can be estimated and provided.
  • FIG. 1 is a schematic view of an image analysis distance information providing system.
  • FIG. 2 is an example of providing estimated distance information.
  • the image analysis distance information providing system analyzes an image of a product taken by a camera, compares the size of the specified product with the number of pixels, and estimates the distance between the product and the camera.
  • FIG. 1 is a schematic view of an image analysis distance information providing system according to a preferred embodiment of the present invention.
  • the image analysis distance information providing system is realized by the control unit reading a predetermined program, an image acquisition unit, an image analysis unit, a type specification unit, a pixel number specification unit, a size acquisition unit, A distance estimation unit and a provision unit are provided.
  • camera information acquisition means, product position estimation means, detection means may be provided. These may be application based, cloud based or otherwise.
  • Each means described above may be realized by a single computer or may be realized by two or more computers (for example, in the case of a server and a terminal).
  • An image acquisition means acquires the image of the product imaged with the camera.
  • the image may be a moving image or a still image.
  • the camera may be a digital camera, a camera of a smartphone, or a camera provided in a drone. Real-time images are preferable for providing distance information in real time.
  • the image analysis means analyzes the image.
  • Machine learning may improve the accuracy of image analysis. For example, machine learning is performed using past images as teacher data.
  • the type identification means identifies the type of product based on the analysis result. As dimensions such as length, width and height differ depending on the type of product, it is necessary to specify the type of product.
  • the product type is, for example, a monitor of FlexScanEV3237-GY of EIZO, a Tokyo Tower, a chair of Lagoon Hard DBR of Nitori, etc.
  • the pixel number specifying means specifies the number of pixels of the product shown in the image based on the analysis result. For example, it is specified that the number of pixels of a product appearing in an image is 120 ⁇ 75 pixels.
  • the angle of view and magnification of the camera may be used to specify the number of pixels of the product appearing in the image. If the angle of view and the magnification are used, the accuracy in specifying the number of pixels is enhanced.
  • the mounting position of the camera, the mounting angle, and the resolution may be used to specify the number of product pixels in the image. The mounting position, mounting angle, and resolution further increase the accuracy of the pixel count.
  • the size obtaining means obtains the size of the product by referring to the Web content based on the type of the specified product. For example, if the specified product type is a monitor of FlexScanEV3237-GY of EIZO, the FlexScanEV3237-GY size is acquired with reference to the EIZO homepage. On the manufacturer's website, dimensions such as length, width, and height are described. In addition, even if it is not the manufacturer's website, even if it refers to the Web content in which dimensions such as length, width, height, etc. are described, such as SNS or second-hand product sales site, the size of the product is acquired Good.
  • the distance estimating means compares the number of pixels of the product shown in the image with the size of the product to estimate the distance between the product and the camera. For example, it is assumed that the number of pixels of the product shown in the image is 120 ⁇ 75 pixels, and the size of the product is 120 cm ⁇ 75 cm. Here, if it is known that a product of 120 centimeters is imaged at 120 pixels when it is 10 meters away from the camera, it can be inferred that the distance between the product and the camera is 10 meters . By using the ratio, it is possible to estimate the distance between the product and the camera, even with other values.
  • the providing means provides the information of the estimated distance. For example, as shown in FIG. 2, information on the distance estimated by being superimposed on the image may be provided. It can be seen that the distance from the camera to the front monitor is 3 mails, and the distance from the camera to the back tower is 220 meters. In addition, only information of the estimated distance without displaying in the image may be provided in association with the type of product. For example, 5 meters to the EIZO FlexScan EV 3237-GY monitor.
  • the camera information acquisition means acquires the camera position and the camera orientation of the camera.
  • a camera position can be acquired from GPS information etc. of a smart phone.
  • the camera position can be obtained from the GPS information or the like provided in the drone.
  • the camera orientation can be acquired from an orientation sensor such as a geomagnetic sensor provided in a smartphone or a drone.
  • the product position estimation means estimates the product position of the product from the camera position and the distance estimated as the camera orientation. Knowing the camera position, the camera orientation, and the distance between the camera and the product, the product position can be inferred based on the camera position.
  • the providing means may provide information of the estimated distance and the estimated product position. Providing information on the distance from the camera to the product and the position of the product increases the value and utilization of the information.
  • the image acquisition unit may acquire a plurality of time-series images. It can be made in time series by the time when the image was acquired. In addition, since time information is often attached as meta information to an image, it can be time-series.
  • the detection means detects the traveling direction and the traveling speed of the product from the plurality of time-series images acquired. If there are a plurality of time-series images, it is possible to detect the traveling direction and traveling speed of the product from the difference.
  • the providing means may provide the information of the estimated distance, the traveling direction of the product, and the traveling speed of the product.
  • the image analysis distance information providing method analyzes an image of a product taken by a camera, compares the size of the specified product with the number of pixels, and estimates the distance between the product and the camera. Is a method to provide.
  • the image analysis distance information providing method includes an image acquisition step, an image analysis step, a type identification step, a pixel number identification step, a size acquisition step, a distance estimation step, and a provision step.
  • a camera information acquisition step, a product position estimation step, and a detection step may be similarly provided.
  • the image acquisition step acquires an image of a product captured by a camera.
  • the image may be a moving image or a still image.
  • the camera may be a digital camera, a camera of a smartphone, or a camera provided in a drone. Real-time images are preferable for providing distance information in real time.
  • the image analysis step analyzes the image.
  • Machine learning may improve the accuracy of image analysis. For example, machine learning is performed using past images as teacher data.
  • the type identification step identifies the type of product based on the analysis result. As dimensions such as length, width and height differ depending on the type of product, it is necessary to specify the type of product.
  • the product type is, for example, a monitor of FlexScanEV3237-GY of EIZO, a Tokyo Tower, a chair of Lagoon Hard DBR of Nitori, etc.
  • the pixel number specifying step specifies the pixel number of the product appearing in the image based on the analysis result. For example, it is specified that the number of pixels of a product appearing in an image is 120 ⁇ 75 pixels. Also, the angle of view and magnification of the camera may be used to specify the number of pixels of the product appearing in the image. If the angle of view and the magnification are used, the accuracy in specifying the number of pixels is enhanced. In addition, the mounting position of the camera, the mounting angle, and the resolution may be used to specify the number of product pixels in the image. The mounting position, mounting angle, and resolution further increase the accuracy of the pixel count.
  • the size obtaining step refers to Web content and obtains the size of the product based on the type of the specified product. For example, if the specified product type is a monitor of FlexScanEV3237-GY of EIZO, the FlexScanEV3237-GY size is acquired with reference to the EIZO homepage. On the manufacturer's website, dimensions such as length, width, and height are described. In addition, even if it is not the manufacturer's website, even if it refers to the Web content in which dimensions such as length, width, height, etc. are described, such as SNS or second-hand product sales site, the size of the product is acquired Good.
  • the distance estimation step compares the number of pixels of the product shown in the image with the size of the product to infer the distance between the product and the camera. For example, it is assumed that the number of pixels of the product shown in the image is 120 ⁇ 75 pixels, and the size of the product is 120 cm ⁇ 75 cm. Here, if it is known that a product of 120 centimeters is imaged at 120 pixels when it is 10 meters away from the camera, it can be inferred that the distance between the product and the camera is 10 meters . By using the ratio, it is possible to estimate the distance between the product and the camera, even with other values.
  • the providing step provides the information of the estimated distance. For example, as shown in FIG. 2, information on the distance estimated by being superimposed on the image may be provided. It can be seen that the distance from the camera to the front monitor is 3 mails, and the distance from the camera to the back tower is 220 meters. In addition, only information of the estimated distance without displaying in the image may be provided in association with the type of product. For example, 5 meters to the EIZO FlexScan EV 3237-GY monitor.
  • the camera information acquisition step acquires the camera position and the camera orientation of the camera.
  • a camera position can be acquired from GPS information etc. of a smart phone.
  • the camera position can be obtained from the GPS information or the like provided in the drone.
  • the camera orientation can be acquired from an orientation sensor such as a geomagnetic sensor provided in a smartphone or a drone.
  • the product position estimation step infers the product position of the product from the camera position and the distance estimated by the camera orientation. Knowing the camera position, the camera orientation, and the distance between the camera and the product, the product position can be inferred based on the camera position.
  • the providing step may provide information of the estimated distance and the estimated product position. Providing information on the distance from the camera to the product and the position of the product increases the value and utilization of the information.
  • the image acquisition step may acquire a plurality of time-series images. It can be made in time series by the time when the image was acquired. In addition, since time information is often attached as meta information to an image, it can be time-series.
  • the detection step detects the traveling direction and the traveling speed of the product from the plurality of time-series images acquired. If there are a plurality of time-series images, it is possible to detect the traveling direction and traveling speed of the product from the difference.
  • the providing step may also provide information on the estimated distance, the direction of travel of the product, and the speed of travel of the product. By providing information on the estimated distance, the direction of product travel, and the speed of product travel, the value and utilization of the information increases.
  • the above-described means and functions are realized by a computer (including a CPU, an information processing device, and various terminals) reading and executing a predetermined program.
  • the program may be, for example, an application installed on a computer, or a SaaS (software as a service) provided from a computer via a network, for example, a flexible disk, a CD It may be provided in the form of being recorded in a computer readable recording medium such as a CD-ROM or the like, a DVD (DVD-ROM, DVD-RAM or the like).
  • the computer reads the program from the recording medium, transfers the program to the internal storage device or the external storage device, stores it, and executes it.
  • the program may be recorded in advance in a storage device (recording medium) such as, for example, a magnetic disk, an optical disk, or a magneto-optical disk, and may be provided from the storage device to the computer via a communication line.
  • nearest neighbor method naive Bayes method
  • decision tree naive Bayes method
  • support vector machine e.g., support vector machine
  • reinforcement learning e.g., reinforcement learning, etc.
  • deep learning may be used in which feature quantities for learning are generated by using a neural network.

Abstract

[Problem] To analyze an image of a product captured by a camera, compare the size of a specified product and the number of pixels, and estimate and provide the distance between the product and the camera. [Solution] Provided is an image analysis distance information provision system comprising: an image acquisition means that obtains an image of a product captured by a camera; an image analysis means that analyses the image; a type specification means that specifies the product type on the basis of the analysis results; a pixel number specification means that specifies the number of pixels for the product appearing in the image, on the basis of the analysis results; a size acquisition means that browses web content and obtains the size of the product, on the basis of the identified product type; a distance estimation means that compares the product size and the number of pixels for the product appearing in the image and estimates the distance between the product and the camera; and a provision means that provides information about the estimated distance.

Description

画像解析距離情報提供システム、画像解析距離情報提供方法およびプログラムImage analysis distance information providing system, image analysis distance information providing method and program
　本発明は、カメラで撮像された製品の画像を解析して、製品とカメラとの間の距離を推測して提供する画像解析距離情報提供システム、画像解析距離情報提供方法およびプログラムに関する。 The present invention relates to an image analysis distance information providing system, an image analysis distance information providing method, and a program for analyzing a product image taken by a camera and estimating and providing a distance between the product and the camera.
　近年、画像解析の技術が進化している。例えば、２次元赤外線センサから得られる熱画像を処理し、検出エリア内の人間等の精度高い情報を検出することを目的とする画像処理装置が提供されている（特許文献１）。 In recent years, techniques for image analysis have evolved. For example, there has been provided an image processing apparatus for processing a thermal image obtained from a two-dimensional infrared sensor and detecting highly accurate information of a person or the like in a detection area (Patent Document 1).
特願平５－２０４９９８号公報Japanese Patent Application No. 5-204998
　しかしながら、特許文献１の装置では、赤外線センサを利用せずに、製品とカメラとの間の距離を推測して提供できない問題がある。 However, in the device of Patent Document 1, there is a problem that the distance between the product and the camera can not be estimated and provided without using an infrared sensor.
　本発明は、上記課題に鑑み、カメラで撮像された製品の画像を解析して、特定された製品のサイズとピクセル数とを比較して、製品とカメラとの間の距離を推測して提供する画像解析距離情報提供システム、画像解析距離情報提供方法およびプログラムを提供することを目的とする。 In view of the above problems, the present invention analyzes an image of a product taken by a camera, compares the size of the specified product with the number of pixels, and estimates and provides the distance between the product and the camera. It is an object of the present invention to provide an image analysis distance information providing system, an image analysis distance information providing method, and a program.
　本発明では、以下のような解決手段を提供する。 The present invention provides the following solutions.
　第１の特徴に係る発明は、カメラで撮像された製品の画像を取得する画像取得手段と、前記画像を解析する画像解析手段と、前記解析の結果に基づいて、前記製品の種類を特定する種類特定手段と、前記解析の結果に基づいて、前記画像に映っている製品のピクセル数を特定するピクセル数特定手段と、前記特定された製品の種類に基づいて、Ｗｅｂコンテンツを参照して当該製品のサイズを取得するサイズ取得手段と、前記画像に映っている製品のピクセル数と、当該製品のサイズと、を比較して、前記製品と前記カメラとの間の距離を推測する距離推測手段と、前記推測された距離の情報を提供する提供手段と、を備える画像解析距離情報提供システムを提供する。 The invention according to the first aspect specifies the type of the product based on an image acquisition unit that acquires an image of a product taken by a camera, an image analysis unit that analyzes the image, and a result of the analysis. The web content is referred to based on the type specifying means, the pixel number specifying means for specifying the number of pixels of the product shown in the image based on the analysis result, and the type of the specified product. Distance estimation means for estimating the distance between the product and the camera by comparing the size acquisition means for acquiring the size of the product, the number of pixels of the product shown in the image, and the size of the product And a providing means for providing information on the estimated distance.
　第１の特徴に係る発明は、カメラで撮像された製品の画像を取得する画像取得ステップと、前記画像を解析する画像解析ステップと、前記解析の結果に基づいて、前記製品の種類を特定する種類特定ステップと、前記解析の結果に基づいて、前記画像に映っている製品のピクセル数を特定するピクセル数特定ステップと、前記特定された製品の種類に基づいて、Ｗｅｂコンテンツを参照して当該製品のサイズを取得するサイズ取得ステップと、前記画像に映っている製品のピクセル数と、当該製品のサイズと、を比較して、前記製品と前記カメラとの間の距離を推測する距離推測ステップと、前記推測された距離の情報を提供する提供ステップと、を備える画像解析距離情報提供方法を提供する。 The invention according to the first aspect specifies the type of the product based on an image acquisition step of acquiring an image of a product taken by a camera, an image analysis step of analyzing the image, and a result of the analysis. Based on the type identification step, the pixel number identification step for identifying the number of pixels of the product appearing in the image based on the result of the analysis, and the web content based on the type of the identified product A distance obtaining step for obtaining a size of a product, and a distance estimating step for estimating a distance between the product and the camera by comparing the number of pixels of the product shown in the image with the size of the product And providing the information of the estimated distance.
　第１の特徴に係る発明は、コンピュータに、カメラで撮像された製品の画像を取得する画像取得ステップと、前記画像を解析する画像解析ステップと、前記解析の結果に基づいて、前記製品の種類を特定する種類特定ステップと、前記解析の結果に基づいて、前記画像に映っている製品のピクセル数を特定するピクセル数特定ステップと、前記特定された製品の種類に基づいて、Ｗｅｂコンテンツを参照して当該製品のサイズを取得するサイズ取得ステップと、前記画像に映っている製品のピクセル数と、当該製品のサイズと、を比較して、前記製品と前記カメラとの間の距離を推測する距離推測ステップと、前記推測された距離の情報を提供する提供ステップと、をさせるためのプログラムを提供する。 The invention according to the first aspect is characterized in that, on a computer, an image acquisition step of acquiring an image of a product taken by a camera, an image analysis step of analyzing the image, and a type of the product based on a result of the analysis. The web content is referred to based on the type identification step of identifying the number of pixels of the product shown in the image based on the analysis result and the type of the identified product. Estimate the distance between the product and the camera by comparing the size of the product with the size of the product in the image acquisition step of acquiring the size of the product, the number of pixels of the product in the image, and the size of the product A program is provided for causing a distance estimation step and a provision step of providing information on the estimated distance.
　カメラで撮像された製品の画像を解析するだけで、製品とカメラとの間の距離を推測して提供できる。 By analyzing the image of the product taken by the camera, the distance between the product and the camera can be estimated and provided.
図１は、画像解析距離情報提供システムの概要図である。FIG. 1 is a schematic view of an image analysis distance information providing system. 図２は、推測された距離の情報を提供した一例である。FIG. 2 is an example of providing estimated distance information.
　以下、本発明を実施するための最良の形態について説明する。なお、これはあくまでも一例であって、本発明の技術的範囲はこれに限られるものではない。 The best mode for carrying out the present invention will be described below. This is merely an example, and the technical scope of the present invention is not limited to this.
　本発明の画像解析距離情報提供システムは、カメラで撮像された製品の画像を解析して、特定された製品のサイズとピクセル数とを比較して、製品とカメラとの間の距離を推測して提供するシステムである。 The image analysis distance information providing system according to the present invention analyzes an image of a product taken by a camera, compares the size of the specified product with the number of pixels, and estimates the distance between the product and the camera. System to provide
　本発明の好適な実施形態の概要について、図１に基づいて説明する。図１は、本発明の好適な実施形態である画像解析距離情報提供システムの概要図である。 An outline of a preferred embodiment of the present invention will be described based on FIG. FIG. 1 is a schematic view of an image analysis distance information providing system according to a preferred embodiment of the present invention.
　図１にあるように、画像解析距離情報提供システムは、制御部が所定のプログラムを読み込むことで実現される、画像取得手段、画像解析手段、種類特定手段、ピクセル数特定手段、サイズ取得手段、距離推測手段、提供手段を備える。また図示しないが、同様に、カメラ情報取得手段、製品位置推測手段、検出手段、を備えてもよい。これらは、アプリケーション型、クラウド型またはその他であってもよい。上述の各手段が、単独のコンピュータで実現されてもよいし、２台以上のコンピュータ（例えば、サーバと端末のような場合）で実現されてもよい。 As shown in FIG. 1, the image analysis distance information providing system is realized by the control unit reading a predetermined program, an image acquisition unit, an image analysis unit, a type specification unit, a pixel number specification unit, a size acquisition unit, A distance estimation unit and a provision unit are provided. Although not shown, in the same manner, camera information acquisition means, product position estimation means, detection means may be provided. These may be application based, cloud based or otherwise. Each means described above may be realized by a single computer or may be realized by two or more computers (for example, in the case of a server and a terminal).
　画像取得手段は、カメラで撮像された製品の画像を取得する。画像は動画でも静止画でもよい。カメラは、デジタルカメラであっても、スマートフォンのカメラであっても、ドローンに備えられたカメラであっても、何でもよい。リアルタイムに距離の情報を提供するにはリアルタイムな画像の方が好ましい。 An image acquisition means acquires the image of the product imaged with the camera. The image may be a moving image or a still image. The camera may be a digital camera, a camera of a smartphone, or a camera provided in a drone. Real-time images are preferable for providing distance information in real time.
　画像解析手段は、画像を解析する。機械学習によって画像解析の精度を向上させてもよい。例えば、過去画像を教師データとして機械学習を行う。 The image analysis means analyzes the image. Machine learning may improve the accuracy of image analysis. For example, machine learning is performed using past images as teacher data.
　種類特定手段は、解析の結果に基づいて、製品の種類を特定する。製品の種類によって長さ・幅・高さなどの寸法が異なるので、製品の種類を特定する必要がある。製品の種類とは、例えば、ＥＩＺＯのＦｌｅｘＳｃａｎＥＶ３２３７－ＧＹのモニタであったり、東京タワーであったり、ニトリのラグーンハード ＤＢＲの椅子であったり、のことである。 The type identification means identifies the type of product based on the analysis result. As dimensions such as length, width and height differ depending on the type of product, it is necessary to specify the type of product. The product type is, for example, a monitor of FlexScanEV3237-GY of EIZO, a Tokyo Tower, a chair of Lagoon Hard DBR of Nitori, etc.
　ピクセル数特定手段は、解析の結果に基づいて、前記画像に映っている製品のピクセル数を特定する。例えば、画像に映っている製品のピクセル数が１２０×７５ピクセルであると特定する。また、カメラの画角および倍率を用いて、画像に映っている製品のピクセル数を特定してもよい。画角と倍率とを用いれば、ピクセル数の特定精度が高まる。更に、カメラの取り付け位置、取り付け角度、および解像度を用いて、画像に映っている製品のピクセル数を特定してもよい。取り付け位置、取り付け角度、および解像度を用いれば、更にピクセル数の特定精度が高まる。 The pixel number specifying means specifies the number of pixels of the product shown in the image based on the analysis result. For example, it is specified that the number of pixels of a product appearing in an image is 120 × 75 pixels. Also, the angle of view and magnification of the camera may be used to specify the number of pixels of the product appearing in the image. If the angle of view and the magnification are used, the accuracy in specifying the number of pixels is enhanced. In addition, the mounting position of the camera, the mounting angle, and the resolution may be used to specify the number of product pixels in the image. The mounting position, mounting angle, and resolution further increase the accuracy of the pixel count.
　サイズ取得手段は、特定された製品の種類に基づいて、Ｗｅｂコンテンツを参照して当該製品のサイズを取得する。例えば、特定された製品の種類がＥＩＺＯのＦｌｅｘＳｃａｎＥＶ３２３７－ＧＹのモニタであれば、ＥＩＺＯのホームページを参照して、ＦｌｅｘＳｃａｎＥＶ３２３７－ＧＹのサイズを取得する。メーカーのホームページには、長さ・幅・高さなどの寸法が記載されている。また、メーカーのホームページでなくても、ＳＮＳや中古製品販売サイトなど、長さ・幅・高さなどの寸法などが記載されているＷｅｂコンテンツを参照して、当該製品のサイズを取得してもよい。 The size obtaining means obtains the size of the product by referring to the Web content based on the type of the specified product. For example, if the specified product type is a monitor of FlexScanEV3237-GY of EIZO, the FlexScanEV3237-GY size is acquired with reference to the EIZO homepage. On the manufacturer's website, dimensions such as length, width, and height are described. In addition, even if it is not the manufacturer's website, even if it refers to the Web content in which dimensions such as length, width, height, etc. are described, such as SNS or second-hand product sales site, the size of the product is acquired Good.
　距離推測手段は、前記画像に映っている製品のピクセル数と、当該製品のサイズと、を比較して、前記製品と前記カメラとの間の距離を推測する。例えば、前記画像に映っている製品のピクセル数が１２０×７５ピクセル、前記製品のサイズが１２０センチメートル×７５センチメートルであったとする。ここで、１２０センチメートルの製品がカメラから１０メートルの離れた場所にある時に１２０ピクセルで撮像されると分かっていれば、前記製品と前記カメラとの間の距離が１０メートルであると推測できる。比を用いることで、他の数値であったとしても製品とカメラとの間の距離を推測できる。 The distance estimating means compares the number of pixels of the product shown in the image with the size of the product to estimate the distance between the product and the camera. For example, it is assumed that the number of pixels of the product shown in the image is 120 × 75 pixels, and the size of the product is 120 cm × 75 cm. Here, if it is known that a product of 120 centimeters is imaged at 120 pixels when it is 10 meters away from the camera, it can be inferred that the distance between the product and the camera is 10 meters . By using the ratio, it is possible to estimate the distance between the product and the camera, even with other values.
　提供手段は、推測された距離の情報を提供する。例えば、図２のように、画像に重畳表示して推測された距離の情報を提供してもよい。カメラから手前のモニタまでの距離が３メール、カメラから奥のタワーまでの距離が２２０メートルであることが分かる。また、画像に表示せずに推測された距離の情報だけを製品の種類と紐付けて提供してもよい。例えば、ＥＩＺＯのＦｌｅｘＳｃａｎＥＶ３２３７－ＧＹのモニタまで５メートルなど。 The providing means provides the information of the estimated distance. For example, as shown in FIG. 2, information on the distance estimated by being superimposed on the image may be provided. It can be seen that the distance from the camera to the front monitor is 3 mails, and the distance from the camera to the back tower is 220 meters. In addition, only information of the estimated distance without displaying in the image may be provided in association with the type of product. For example, 5 meters to the EIZO FlexScan EV 3237-GY monitor.
　カメラ情報取得手段は、カメラのカメラ位置とカメラ向きとを取得する。例えば、スマートフォンのカメラであれば、スマートフォンのＧＰＳ情報などからカメラ位置を取得できる。例えば、ドローンに備えられたカメラであれば、ドローンに備えられたＧＰＳ情報などからカメラ位置を取得できる。同様に、カメラ向きは、スマートフォンやドローンなどに備えられた地磁気センサなどの向きセンサから取得できる。 The camera information acquisition means acquires the camera position and the camera orientation of the camera. For example, if it is a camera of a smart phone, a camera position can be acquired from GPS information etc. of a smart phone. For example, if the camera is provided in the drone, the camera position can be obtained from the GPS information or the like provided in the drone. Similarly, the camera orientation can be acquired from an orientation sensor such as a geomagnetic sensor provided in a smartphone or a drone.
　製品位置推測手段は、カメラ位置とカメラ向きと推測された距離とから、製品の製品位置を推測する。カメラ位置と、カメラ向きと、カメラと製品との間の距離と、が分かれば、カメラの位置を基準にして製品位置を推測することができる。 The product position estimation means estimates the product position of the product from the camera position and the distance estimated as the camera orientation. Knowing the camera position, the camera orientation, and the distance between the camera and the product, the product position can be inferred based on the camera position.
　また、提供手段は、推測された距離と推測された製品位置との情報を提供してもよい。カメラから製品までの距離と、製品の位置と、の情報を提供することで、情報の価値や活用度が増える。 Also, the providing means may provide information of the estimated distance and the estimated product position. Providing information on the distance from the camera to the product and the position of the product increases the value and utilization of the information.
　また、画像取得手段は、時系列の複数枚の前記画像を取得してもよい。画像を取得した時間で時系列に出来る。また、画像に時間情報がメタ情報として付与されていることも多いので時系列に出来る。 Further, the image acquisition unit may acquire a plurality of time-series images. It can be made in time series by the time when the image was acquired. In addition, since time information is often attached as meta information to an image, it can be time-series.
　検出手段は、取得された時系列の複数枚の画像から、製品の進行方向と進行速度を検出する。時系列の複数枚の画像があれば、その差分から製品の進行方向と進行速度を検出できる。 The detection means detects the traveling direction and the traveling speed of the product from the plurality of time-series images acquired. If there are a plurality of time-series images, it is possible to detect the traveling direction and traveling speed of the product from the difference.
　また、提供手段は、推測された距離と、製品の進行方向と、製品の進行速度と、の情報を提供してもよい。推測された距離と、製品の進行方向と、製品の進行速度と、の情報を提供することで、情報の価値や活用度が増える。
[動作の説明] Also, the providing means may provide the information of the estimated distance, the traveling direction of the product, and the traveling speed of the product. By providing information on the estimated distance, the direction of product travel, and the speed of product travel, the value and utilization of the information increases.
[Description of operation]
　次に、画像解析距離情報提供方法について説明する。本発明の画像解析距離情報提供方法は、カメラで撮像された製品の画像を解析して、特定された製品のサイズとピクセル数とを比較して、製品とカメラとの間の距離を推測して提供する方法である。 Next, a method of providing image analysis distance information will be described. The image analysis distance information providing method according to the present invention analyzes an image of a product taken by a camera, compares the size of the specified product with the number of pixels, and estimates the distance between the product and the camera. Is a method to provide.
　画像解析距離情報提供方法は、画像取得ステップ、画像解析ステップ、種類特定ステップ、ピクセル数特定ステップ、サイズ取得ステップ、距離推測ステップ、提供ステップを備える。また図示しないが、同様に、カメラ情報取得ステップ、製品位置推測ステップ、検出ステップ、を備えてもよい。 The image analysis distance information providing method includes an image acquisition step, an image analysis step, a type identification step, a pixel number identification step, a size acquisition step, a distance estimation step, and a provision step. Although not shown, a camera information acquisition step, a product position estimation step, and a detection step may be similarly provided.
　画像取得ステップは、カメラで撮像された製品の画像を取得する。画像は動画でも静止画でもよい。カメラは、デジタルカメラであっても、スマートフォンのカメラであっても、ドローンに備えられたカメラであっても、何でもよい。リアルタイムに距離の情報を提供するにはリアルタイムな画像の方が好ましい。 The image acquisition step acquires an image of a product captured by a camera. The image may be a moving image or a still image. The camera may be a digital camera, a camera of a smartphone, or a camera provided in a drone. Real-time images are preferable for providing distance information in real time.
　画像解析ステップは、画像を解析する。機械学習によって画像解析の精度を向上させてもよい。例えば、過去画像を教師データとして機械学習を行う。 The image analysis step analyzes the image. Machine learning may improve the accuracy of image analysis. For example, machine learning is performed using past images as teacher data.
　種類特定ステップは、解析の結果に基づいて、製品の種類を特定する。製品の種類によって長さ・幅・高さなどの寸法が異なるので、製品の種類を特定する必要がある。製品の種類とは、例えば、ＥＩＺＯのＦｌｅｘＳｃａｎＥＶ３２３７－ＧＹのモニタであったり、東京タワーであったり、ニトリのラグーンハード ＤＢＲの椅子であったり、のことである。 The type identification step identifies the type of product based on the analysis result. As dimensions such as length, width and height differ depending on the type of product, it is necessary to specify the type of product. The product type is, for example, a monitor of FlexScanEV3237-GY of EIZO, a Tokyo Tower, a chair of Lagoon Hard DBR of Nitori, etc.
　ピクセル数特定ステップは、解析の結果に基づいて、前記画像に映っている製品のピクセル数を特定する。例えば、画像に映っている製品のピクセル数が１２０×７５ピクセルであると特定する。また、カメラの画角および倍率を用いて、画像に映っている製品のピクセル数を特定してもよい。画角と倍率とを用いれば、ピクセル数の特定精度が高まる。更に、カメラの取り付け位置、取り付け角度、および解像度を用いて、画像に映っている製品のピクセル数を特定してもよい。取り付け位置、取り付け角度、および解像度を用いれば、更にピクセル数の特定精度が高まる。 The pixel number specifying step specifies the pixel number of the product appearing in the image based on the analysis result. For example, it is specified that the number of pixels of a product appearing in an image is 120 × 75 pixels. Also, the angle of view and magnification of the camera may be used to specify the number of pixels of the product appearing in the image. If the angle of view and the magnification are used, the accuracy in specifying the number of pixels is enhanced. In addition, the mounting position of the camera, the mounting angle, and the resolution may be used to specify the number of product pixels in the image. The mounting position, mounting angle, and resolution further increase the accuracy of the pixel count.
　サイズ取得ステップは、特定された製品の種類に基づいて、Ｗｅｂコンテンツを参照して当該製品のサイズを取得する。例えば、特定された製品の種類がＥＩＺＯのＦｌｅｘＳｃａｎＥＶ３２３７－ＧＹのモニタであれば、ＥＩＺＯのホームページを参照して、ＦｌｅｘＳｃａｎＥＶ３２３７－ＧＹのサイズを取得する。メーカーのホームページには、長さ・幅・高さなどの寸法が記載されている。また、メーカーのホームページでなくても、ＳＮＳや中古製品販売サイトなど、長さ・幅・高さなどの寸法などが記載されているＷｅｂコンテンツを参照して、当該製品のサイズを取得してもよい。 The size obtaining step refers to Web content and obtains the size of the product based on the type of the specified product. For example, if the specified product type is a monitor of FlexScanEV3237-GY of EIZO, the FlexScanEV3237-GY size is acquired with reference to the EIZO homepage. On the manufacturer's website, dimensions such as length, width, and height are described. In addition, even if it is not the manufacturer's website, even if it refers to the Web content in which dimensions such as length, width, height, etc. are described, such as SNS or second-hand product sales site, the size of the product is acquired Good.
　距離推測ステップは、前記画像に映っている製品のピクセル数と、当該製品のサイズと、を比較して、前記製品と前記カメラとの間の距離を推測する。例えば、前記画像に映っている製品のピクセル数が１２０×７５ピクセル、前記製品のサイズが１２０センチメートル×７５センチメートルであったとする。ここで、１２０センチメートルの製品がカメラから１０メートルの離れた場所にある時に１２０ピクセルで撮像されると分かっていれば、前記製品と前記カメラとの間の距離が１０メートルであると推測できる。比を用いることで、他の数値であったとしても製品とカメラとの間の距離を推測できる。 The distance estimation step compares the number of pixels of the product shown in the image with the size of the product to infer the distance between the product and the camera. For example, it is assumed that the number of pixels of the product shown in the image is 120 × 75 pixels, and the size of the product is 120 cm × 75 cm. Here, if it is known that a product of 120 centimeters is imaged at 120 pixels when it is 10 meters away from the camera, it can be inferred that the distance between the product and the camera is 10 meters . By using the ratio, it is possible to estimate the distance between the product and the camera, even with other values.
　提供ステップは、推測された距離の情報を提供する。例えば、図２のように、画像に重畳表示して推測された距離の情報を提供してもよい。カメラから手前のモニタまでの距離が３メール、カメラから奥のタワーまでの距離が２２０メートルであることが分かる。また、画像に表示せずに推測された距離の情報だけを製品の種類と紐付けて提供してもよい。例えば、ＥＩＺＯのＦｌｅｘＳｃａｎＥＶ３２３７－ＧＹのモニタまで５メートルなど。 The providing step provides the information of the estimated distance. For example, as shown in FIG. 2, information on the distance estimated by being superimposed on the image may be provided. It can be seen that the distance from the camera to the front monitor is 3 mails, and the distance from the camera to the back tower is 220 meters. In addition, only information of the estimated distance without displaying in the image may be provided in association with the type of product. For example, 5 meters to the EIZO FlexScan EV 3237-GY monitor.
　カメラ情報取得ステップは、カメラのカメラ位置とカメラ向きとを取得する。例えば、スマートフォンのカメラであれば、スマートフォンのＧＰＳ情報などからカメラ位置を取得できる。例えば、ドローンに備えられたカメラであれば、ドローンに備えられたＧＰＳ情報などからカメラ位置を取得できる。同様に、カメラ向きは、スマートフォンやドローンなどに備えられた地磁気センサなどの向きセンサから取得できる。 The camera information acquisition step acquires the camera position and the camera orientation of the camera. For example, if it is a camera of a smart phone, a camera position can be acquired from GPS information etc. of a smart phone. For example, if the camera is provided in the drone, the camera position can be obtained from the GPS information or the like provided in the drone. Similarly, the camera orientation can be acquired from an orientation sensor such as a geomagnetic sensor provided in a smartphone or a drone.
　製品位置推測ステップは、カメラ位置とカメラ向きと推測された距離とから、製品の製品位置を推測する。カメラ位置と、カメラ向きと、カメラと製品との間の距離と、が分かれば、カメラの位置を基準にして製品位置を推測することができる。 The product position estimation step infers the product position of the product from the camera position and the distance estimated by the camera orientation. Knowing the camera position, the camera orientation, and the distance between the camera and the product, the product position can be inferred based on the camera position.
　また、提供ステップは、推測された距離と推測された製品位置との情報を提供してもよい。カメラから製品までの距離と、製品の位置と、の情報を提供することで、情報の価値や活用度が増える。 Also, the providing step may provide information of the estimated distance and the estimated product position. Providing information on the distance from the camera to the product and the position of the product increases the value and utilization of the information.
　また、画像取得ステップは、時系列の複数枚の前記画像を取得してもよい。画像を取得した時間で時系列に出来る。また、画像に時間情報がメタ情報として付与されていることも多いので時系列に出来る。 Further, the image acquisition step may acquire a plurality of time-series images. It can be made in time series by the time when the image was acquired. In addition, since time information is often attached as meta information to an image, it can be time-series.
　検出ステップは、取得された時系列の複数枚の画像から、製品の進行方向と進行速度を検出する。時系列の複数枚の画像があれば、その差分から製品の進行方向と進行速度を検出できる。 The detection step detects the traveling direction and the traveling speed of the product from the plurality of time-series images acquired. If there are a plurality of time-series images, it is possible to detect the traveling direction and traveling speed of the product from the difference.
　また、提供ステップは、推測された距離と、製品の進行方向と、製品の進行速度と、の情報を提供してもよい。推測された距離と、製品の進行方向と、製品の進行速度と、の情報を提供することで、情報の価値や活用度が増える。 The providing step may also provide information on the estimated distance, the direction of travel of the product, and the speed of travel of the product. By providing information on the estimated distance, the direction of product travel, and the speed of product travel, the value and utilization of the information increases.
　上述した手段、機能は、コンピュータ（ＣＰＵ、情報処理装置、各種端末を含む）が、所定のプログラムを読み込んで、実行することによって実現される。プログラムは、例えば、コンピュータにインストールされるアプリケーションであってもよいし、コンピュータからネットワーク経由で提供されるＳａａＳ（ソフトウェア・アズ・ア・サービス）形態であってもよいし、例えば、フレキシブルディスク、ＣＤ（ＣＤ－ＲＯＭなど）、ＤＶＤ（ＤＶＤ－ＲＯＭ、ＤＶＤ－ＲＡＭなど）等のコンピュータ読取可能な記録媒体に記録された形態で提供されてもよい。この場合、コンピュータはその記録媒体からプログラムを読み取って内部記憶装置または外部記憶装置に転送し記憶して実行する。また、そのプログラムを、例えば、磁気ディスク、光ディスク、光磁気ディスク等の記憶装置（記録媒体）に予め記録しておき、その記憶装置から通信回線を介してコンピュータに提供するようにしてもよい。 The above-described means and functions are realized by a computer (including a CPU, an information processing device, and various terminals) reading and executing a predetermined program. The program may be, for example, an application installed on a computer, or a SaaS (software as a service) provided from a computer via a network, for example, a flexible disk, a CD It may be provided in the form of being recorded in a computer readable recording medium such as a CD-ROM or the like, a DVD (DVD-ROM, DVD-RAM or the like). In this case, the computer reads the program from the recording medium, transfers the program to the internal storage device or the external storage device, stores it, and executes it. Alternatively, the program may be recorded in advance in a storage device (recording medium) such as, for example, a magnetic disk, an optical disk, or a magneto-optical disk, and may be provided from the storage device to the computer via a communication line.
　上述した機械学習の具体的なアルゴリズムとしては、最近傍法、ナイーブベイズ法、決定木、サポートベクターマシン、強化学習などを利用してよい。また、ニューラルネットワークを利用して、学習するための特徴量を自ら生成する深層学習（ディープラーニング）であってもよい。 As a specific algorithm of the above-mentioned machine learning, nearest neighbor method, naive Bayes method, decision tree, support vector machine, reinforcement learning, etc. may be used. In addition, deep learning may be used in which feature quantities for learning are generated by using a neural network.
　以上、本発明の実施形態について説明したが、本発明は上述したこれらの実施形態に限るものではない。また、本発明の実施形態に記載された効果は、本発明から生じる最も好適な効果を列挙したに過ぎず、本発明による効果は、本発明の実施形態に記載されたものに限定されるものではない。

  As mentioned above, although embodiment of this invention was described, this invention is not limited to these embodiment mentioned above. Further, the effects described in the embodiments of the present invention only list the most preferable effects resulting from the present invention, and the effects according to the present invention are limited to those described in the embodiments of the present invention is not.

Claims (8)

  1. 　カメラで撮像された製品の画像を取得する画像取得手段と、
    　前記画像を解析する画像解析手段と、
    　前記解析の結果に基づいて、前記製品の種類を特定する種類特定手段と、
    　前記解析の結果に基づいて、前記画像に映っている製品のピクセル数を特定するピクセル数特定手段と、
    　前記特定された製品の種類に基づいて、Ｗｅｂコンテンツを参照して当該製品のサイズを取得するサイズ取得手段と、
    　前記画像に映っている製品のピクセル数と、当該製品のサイズと、を比較して、前記製品と前記カメラとの間の距離を推測する距離推測手段と、
    　前記推測された距離の情報を提供する提供手段と、
    を備える画像解析距離情報提供システム。     An image acquisition unit that acquires an image of a product captured by a camera;
    Image analysis means for analyzing the image;
    Type identification means for identifying the type of the product based on the result of the analysis;
    Pixel number specifying means for specifying the number of pixels of the product shown in the image based on the result of the analysis;
    Size acquiring means for acquiring the size of the product by referring to Web content based on the type of the specified product;
    Distance estimation means for estimating the distance between the product and the camera by comparing the number of pixels of the product shown in the image with the size of the product;
    Providing means for providing information of the estimated distance;
    Image analysis distance information providing system comprising:
  2. 　前記ピクセル数特定手段は、前記カメラの画角および倍率を用いて、前記画像に映っている製品のピクセル数を特定する請求項１に記載の画像解析距離情報提供システム。 The image analysis distance information providing system according to claim 1, wherein the pixel number specifying unit specifies the number of pixels of a product appearing in the image using an angle of view and a magnification of the camera.
  3. 　前記ピクセル数特定手段は、更に、前記カメラの取り付け位置、取り付け角度、および解像度を用いて、前記画像に映っている製品のピクセル数を特定する
    請求項１に記載の画像解析距離情報提供システム。     The image analysis distance information providing system according to claim 1, wherein the pixel number specifying means further specifies the number of pixels of a product shown in the image using the mounting position, mounting angle and resolution of the camera.
  4. 　前記カメラのカメラ位置とカメラ向きと、を取得するカメラ情報取得手段と、
    　前記カメラ位置と前記カメラ向きと前記推測された距離とから、前記製品の製品位置を推測する製品位置推測手段と、
    を備え、
    　前記提供手段は、前記推測された距離と、前記推測された製品位置と、の情報を提供する
    請求項１に記載の画像解析距離情報提供システム。     Camera information acquisition means for acquiring the camera position and the camera orientation of the camera;
    Product position estimation means for estimating a product position of the product from the camera position, the camera direction, and the estimated distance;
    Equipped with
    The image analysis distance information providing system according to claim 1, wherein the providing means provides information on the estimated distance and the estimated product position.
  5. 　前記画像取得手段は、時系列の複数枚の前記画像を取得し、
    　前記取得された時系列の複数枚の画像から、前記製品の進行方向と進行速度を検出する検出手段を備え、
    　前記提供手段は、前記推測された距離と、前記製品の進行方向と、前記製品の進行速度と、の情報を提供する
    請求項１に記載の画像解析距離情報提供システム。     The image acquisition unit acquires a plurality of time-series images.
    A detection unit configured to detect the traveling direction and the traveling speed of the product from the plurality of time-series acquired images;
    The image analysis distance information providing system according to claim 1, wherein the providing means provides information on the estimated distance, the traveling direction of the product, and the traveling speed of the product.
  6. 　前記提供手段は、前記画像に重畳表示して、前記推測された距離の情報を提供する
    請求項１に記載の画像解析距離情報提供システム。     The image analysis distance information providing system according to claim 1, wherein the providing means provides information of the estimated distance by superimposing on the image.
  7. 　カメラで撮像された製品の画像を取得する画像取得ステップと、
    　前記画像を解析する画像解析ステップと、
    　前記解析の結果に基づいて、前記製品の種類を特定する種類特定ステップと、
    　前記解析の結果に基づいて、前記画像に映っている製品のピクセル数を特定するピクセル数特定ステップと、
    　前記特定された製品の種類に基づいて、Ｗｅｂコンテンツを参照して当該製品のサイズを取得するサイズ取得ステップと、
    　前記画像に映っている製品のピクセル数と、当該製品のサイズと、を比較して、前記製品と前記カメラとの間の距離を推測する距離推測ステップと、
    　前記推測された距離の情報を提供する提供ステップと、
    を備える画像解析距離情報提供方法。     An image acquisition step of acquiring an image of a product taken by a camera;
    An image analysis step of analyzing the image;
    A type identification step of identifying the type of the product based on the result of the analysis;
    A pixel number identification step of identifying a pixel number of a product appearing in the image based on a result of the analysis;
    A size obtaining step of obtaining the size of the product by referring to the Web content based on the type of the specified product;
    A distance estimation step of estimating the distance between the product and the camera by comparing the number of pixels of the product shown in the image with the size of the product;
    Providing information of the estimated distance;
    Image analysis distance information providing method comprising:
  8. 　コンピュータに、
    　カメラで撮像された製品の画像を取得する画像取得ステップと、
    　前記画像を解析する画像解析ステップと、
    　前記解析の結果に基づいて、前記製品の種類を特定する種類特定ステップと、
    　前記解析の結果に基づいて、前記画像に映っている製品のピクセル数を特定するピクセル数特定ステップと、
    　前記特定された製品の種類に基づいて、Ｗｅｂコンテンツを参照して当該製品のサイズを取得するサイズ取得ステップと、
    　前記画像に映っている製品のピクセル数と、当該製品のサイズと、を比較して、前記製品と前記カメラとの間の距離を推測する距離推測ステップと、
    　前記推測された距離の情報を提供する提供ステップと、
    を実行させるためのプログラム。
          On the computer
    An image acquisition step of acquiring an image of a product taken by a camera;
    An image analysis step of analyzing the image;
    A type identification step of identifying the type of the product based on the result of the analysis;
    A pixel number identification step of identifying a pixel number of a product appearing in the image based on a result of the analysis;
    A size obtaining step of obtaining the size of the product by referring to the Web content based on the type of the specified product;
    A distance estimation step of estimating the distance between the product and the camera by comparing the number of pixels of the product shown in the image with the size of the product;
    Providing information of the estimated distance;
    A program to run a program.
PCT/JP2017/031381 2017-08-31 2017-08-31 Image analysis distance information provision system, image analysis distance information provision method, and program WO2019043879A1 (en)

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JPH06255423A (en) * 1993-03-04 1994-09-13 Sharp Corp On-vehicle monitor camera device
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