WO2021054266A1 - 画像処理装置、画像処理方法、及びプログラム - Google Patents

画像処理装置、画像処理方法、及びプログラム Download PDF

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Publication number
WO2021054266A1
WO2021054266A1 PCT/JP2020/034551 JP2020034551W WO2021054266A1 WO 2021054266 A1 WO2021054266 A1 WO 2021054266A1 JP 2020034551 W JP2020034551 W JP 2020034551W WO 2021054266 A1 WO2021054266 A1 WO 2021054266A1
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WIPO (PCT)
Prior art keywords
article
image processing
unspecified
shelf
product name
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Ceased
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PCT/JP2020/034551
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English (en)
French (fr)
Japanese (ja)
Inventor
八栄子 米澤
開登 堀田
隆亮 岩渕
明子 久保
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NEC Corp
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NEC Corp
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Publication date
Application filed by NEC Corp filed Critical NEC Corp
Priority to JP2021546649A priority Critical patent/JP7613360B2/ja
Priority to US17/641,045 priority patent/US12211247B2/en
Publication of WO2021054266A1 publication Critical patent/WO2021054266A1/ja
Anticipated expiration legal-status Critical
Priority to US18/219,498 priority patent/US12299954B2/en
Priority to US18/220,038 priority patent/US12299955B2/en
Priority to US19/173,956 priority patent/US20250239046A1/en
Priority to US19/174,004 priority patent/US20250239047A1/en
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/63—Scene text, e.g. street names

Definitions

  • the present invention relates to an image processing apparatus, an image processing method, and a program.
  • Patent Document 1 describes that the displayed article is recognized by processing the photographed image of the displayed article. Patent Document 1 further describes that, when there is an unrecognized article, an article surrounding the article and already recognized is used as a candidate for the article.
  • the present inventor considered estimating the product name of the article with high accuracy when there is an article that was not recognized by the image processing.
  • An object of the present invention is to estimate the product name of an article with high accuracy when there is an article that is not recognized by image processing.
  • an image processing means for identifying the product name of each of the plurality of articles shown in the image by processing an image obtained by photographing a plurality of articles arranged on the shelf.
  • the product name of the unspecified article is changed to the product name of the article located next to the unspecified article.
  • Goods estimation means to estimate and An image processing apparatus comprising the above is provided.
  • the computer By processing images of a plurality of articles placed on the shelves, the product names of the plurality of articles shown in the images can be identified.
  • Image processing that estimates the product name of the unspecified article as the product name of the article located next to the unspecified article when the specific conditions are satisfied for the unspecified article that cannot be specified. A method is provided.
  • a computer A function of identifying the product name of each of the plurality of articles shown in the image by processing an image of a plurality of articles arranged on the shelf, and a function of identifying the product name of each of the plurality of articles shown in the image.
  • the product name of the article when there is an article that is not recognized by image processing, the product name of the article can be estimated with high accuracy.
  • FIG. 1 is a diagram showing an example of the functional configuration of the image processing device 10 according to the embodiment.
  • the image processing device 10 includes an image processing unit 120 and an article estimation unit 130.
  • the image processing unit 120 identifies the product name (for example, product name) of each of the plurality of articles shown in the image by processing an image obtained by photographing a plurality of articles (for example, products) arranged on the shelf. ..
  • specifying the product name also includes specifying the identification information associated with the product name.
  • the article estimation unit 130 satisfies the specific conditions for the article whose product name could not be specified by the image processing unit 120 (hereinafter referred to as unspecified article)
  • the article estimation unit 130 puts the item name of the unspecified article next to the unspecified article. It is presumed to be the product name of the article located in.
  • the image processing apparatus 10 will be described in detail.
  • the image processing device 10 has an acquisition unit 110, a reference data storage unit 140, a result storage unit 142, and an output unit 150 in addition to the image processing unit 120 and the article estimation unit 130 described above.
  • the acquisition unit 110 acquires an image processed by the image processing unit 120, that is, an image including a plurality of articles from the user terminal 20.
  • the user terminal 20 is, for example, a portable terminal possessed by the user of the image processing device 10, and has a photographing function. The user takes a picture of the shelf in the store and generates an image. This image contains multiple items placed on shelves.
  • the user terminal 20 transmits the generated image to the image processing device 10.
  • the user terminal 20 includes information for specifying the store where the shelf to be photographed is installed (hereinafter referred to as store identification information) and conditions for generating the image (hereinafter referred to as generation conditions). Described) is sent.
  • the generation condition includes at least the generation date and time of the image.
  • the acquisition unit 110 also receives the store identification information and the generation conditions.
  • the image processing unit 120 processes the image transmitted by the user terminal 20 to specify the product name of the article included in the image. For example, the image processing unit 120 identifies the regions of each of the plurality of articles included in the image, and specifies the product name of each of the plurality of articles by performing feature quantity matching for each of the specified plurality of articles.
  • the reference data storage unit 140 stores the feature amount for each article used here and the feature amount for specifying the area of the article. Then, the image processing unit 120 stores the information including the specific result in the result storage unit 142.
  • the image processing unit 120 may specify the product name of the article by using the result of machine learning.
  • the result of machine learning is stored in the reference data storage unit 140.
  • the image processing unit 120 when the image processing unit 120 is presumed to have an article but cannot specify the item name of the article (for example, when there is no item name whose score is equal to or higher than the reference value), the area of the article, that is, the unspecified article.
  • the information indicating the above, the product name of the article located around the unspecified article, and the position thereof are output to the article estimation unit 130.
  • the article estimation unit 130 estimates the item name of the unspecified article as the item name of the article located next to the unspecified article when the specific condition is satisfied. A specific example of this specific condition will be described later.
  • the article estimation unit 130 stores the estimation result in the result storage unit 142.
  • the output unit 150 outputs the estimation result by the article estimation unit 130 to the user terminal 20.
  • the output unit 150 receives information from the user terminal 20 indicating that the estimation result by the article estimation unit 130 is correct
  • the output unit 150 tells the result storage unit 142 that the estimation result by the article estimation unit 130, that is, the product name of the unspecified article. May be memorized.
  • the article estimation unit 130 does not have to store the estimation result in the result storage unit 142.
  • the output unit 150 reads out the information stored in the result storage unit 142 and outputs it to the user terminal 20 as necessary.
  • the output unit 150 may output the data stored in the result storage unit 142 to a terminal other than the user terminal 20.
  • FIG. 2 is a diagram showing an example of the data structure of the result storage unit 142.
  • the result storage unit 142 stores the image acquired by the acquisition unit 110 and the generation conditions (for example, date and time) of the image, the position of the article included in the image, and the product name of the article. Further, when the product name of the article is estimated by the article estimation unit 130, the result storage unit 142 also stores information indicating that fact in association with the article.
  • the position of the unspecified article is stored in the result storage unit 142, but the product name of the unspecified article is not stored.
  • FIG. 3 is a block diagram illustrating the hardware configuration of the image processing device 10.
  • the image processing device 10 includes a bus 1010, a processor 1020, a memory 1030, a storage device 1040, an input / output interface 1050, and a network interface 1060.
  • the bus 1010 is a data transmission path for the processor 1020, the memory 1030, the storage device 1040, the input / output interface 1050, and the network interface 1060 to transmit and receive data to and from each other.
  • the method of connecting the processors 1020 and the like to each other is not limited to the bus connection.
  • the processor 1020 is a processor realized by a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or the like.
  • the memory 1030 is a main storage device realized by a RAM (Random Access Memory) or the like.
  • the storage device 1040 is an auxiliary storage device realized by an HDD (Hard Disk Drive), an SSD (Solid State Drive), a memory card, a ROM (Read Only Memory), or the like.
  • the storage device 1040 stores a program module that realizes each function of the image processing device 10 (for example, an acquisition unit 110, an image processing unit 120, an article estimation unit 130, and an output unit 150).
  • the processor 1020 reads each of these program modules into the memory 1030 and executes them, each function corresponding to the program module is realized.
  • the storage device 1040 also functions as various storage units (for example, a reference data storage unit 140 and a result storage unit 142).
  • the input / output interface 1050 is an interface for connecting the image processing device 10 and various input / output devices.
  • the network interface 1060 is an interface for connecting the image processing device 10 to another device (for example, a user terminal 20) on the network.
  • FIG. 4 is a flowchart showing an example of processing performed by the image processing device 10.
  • the user of the user terminal 20 takes a picture of the shelf at a desired store (for example, a store where he / she works or supervises), and the image generated by this picture is used as the store specific information of the store and the image generation condition (for example, the generation date and time). ), And is transmitted to the image processing device 10. This process may be performed each time an image is generated. Further, the user of the user terminal 20 may take pictures of different places on the shelves and transmit a plurality of images generated by these pictures to the image processing device 10 together.
  • a desired store for example, a store where he / she works or supervises
  • the image generated by this picture is used as the store specific information of the store and the image generation condition (for example, the generation date and time).
  • the acquisition unit 110 of the image processing device 10 acquires the image, the image generation condition, and the store identification information (step S10). Then, the image processing unit 120 of the image processing device 10 identifies the area of the article by processing the image acquired by the acquisition unit 110 (step S20). Here, when a plurality of articles are included in the image, the image processing unit 120 identifies each of the regions of the plurality of articles. Next, the image processing unit 120 and the article estimation unit 130 specify the product name of the article by processing the region of the identified article (step S30). Next, the image processing unit 120 and the article estimation unit 130 store the article name specified in step S30 in the reference data storage unit 140 (step S40).
  • step S20 When a plurality of shelf label areas are specified in step S20, the processing shown in step S30 is performed for each of these plurality of shelf label areas. Further, the process shown in step S20 may be performed using the result of machine learning.
  • FIG. 5 is a flowchart showing a first example of step S30 of FIG.
  • the image processing unit 120 collates the area of the article specified in step S20 with the feature amount stored in the result storage unit 142, and calculates a score for each product name (step S100).
  • the process shown in step S100 may be performed using the result of machine learning.
  • the product name corresponding to the feature amount having the score equal to or higher than the reference value and the maximum is specified as the product name of the article located in the area (step S110: Yes), and the specified product name is used as the information for specifying the area and the product name.
  • the result storage unit 142 stores the image in association with the image to be processed (step S140).
  • step S110 When there is no feature amount whose score is equal to or higher than the reference value (step S110: No), the article estimation unit 130 determines whether or not the region satisfies the specific condition (step S120). Specific examples of specific conditions will be described later with reference to FIGS. 6 to 9.
  • the article estimation unit 130 estimates the article name of the article (unspecified article) in the area as the article name of the article located next to the unspecified article (step). S130). Then, the article estimation unit 130 stores the estimated product name in the result storage unit 142 in association with the information specifying the area and the image to be processed (step S140). At this time, the article estimation unit 130 also stores the information indicating that the product name has been estimated by the article estimation unit 130 in the result storage unit 142.
  • the article estimation unit 130 stores the information for specifying the area in the result storage unit 142 in association with the image to be processed (step S140). .. In this case, the product name column is substantially blank.
  • FIG. 6 is a diagram showing a first example of the specific condition in step S120.
  • a plurality of articles 40 are arranged on the same shelf 30.
  • the specific condition is the case where the product names of the articles 40 on both sides of the unspecified article 40a are the same.
  • FIG. 7 is a diagram showing a second example of the specific condition in step S120.
  • the specific condition is that the unspecified article 40a is located at the end of the shelf 30.
  • the specific condition is that the distance between the unspecified article 40a and the side surface 32 of the product shelf is equal to or less than the reference value, and there is no other article between the side surface 32 and the unspecified article 40a.
  • the reason why the example shown in this figure is set as a specific condition is that the light of the light source in the store is reflected by the side surface 32 or the lighting is embedded in the side surface 32, which corresponds to the unspecified article 40a in the image. This is because the area to be used may be overexposed.
  • FIG. 8 is a diagram showing a third example of the specific condition in step S120.
  • the image includes a first shelf 30a and a shelf immediately above the first shelf 30a (hereinafter, referred to as a second shelf 30b).
  • the specific conditions are a first unspecified article 40a-1 arranged on the first shelf 30a, a second unspecified article 40a-2 arranged on the second shelf, and the like.
  • the difference between the positions of the first unspecified articles 40a-1 and the second unspecified articles 40a-2 is equal to or less than the reference value, that is, the first unspecified articles 40a-1 and the second unspecified articles 40a-2 are arranged at substantially the same positions in the lateral direction.
  • the specific condition is a case where the unspecified articles 40a-1 and 40a-2 are both located at the edge of the shelf 30.
  • the reason for setting the example shown in this figure as a specific condition is the same as that shown in FIG. 7.
  • FIG. 9 is a diagram showing a fourth example of the specific condition in step S120.
  • the specific condition is that the arrangement of the product names of the plurality of articles 40 is regular, and the unspecified article 40a is presumed to have the same product name as the adjacent article 40 in this regularity. is there.
  • FIG. 10 is a flowchart showing a second example of step S30 of FIG.
  • the processes from step S100 to step S130 and the process shown in step S140 are the same as those in the first example shown in FIG.
  • the output unit 150 determines the product name estimated by the article estimation unit 130. It is output to the user terminal 20 together with the image of the unspecified article (step S132). It is preferable that the article estimation unit 130 does not store the estimation result of the product name of the unspecified article 40a in the result storage unit 142.
  • the user terminal 20 displays the product name received from the output unit 150 (that is, the product name estimated by the article estimation unit 130) together with the image of the unspecified article.
  • the user of the user terminal 20 confirms that the product name is correct, he / she inputs to the user terminal 20 to that effect. Then, the user terminal 20 transmits information indicating that the product name is correct to the image processing device 10.
  • the output unit 150 When the output unit 150 receives information from the user terminal 20 indicating that the estimation result by the article estimation unit 130 is correct (step S134), the output unit 150 tells the result storage unit 142 that the estimation result by the article estimation unit 130, that is, the unspecified article.
  • the product name is stored (step S140).
  • the user of the user terminal 20 preferably inputs the correct product name and transmits it to the image processing device 10.
  • the output unit 150 may store the correct product name transmitted from the user terminal 20 in the result storage unit 142.
  • FIG. 11 is a diagram showing an example of a screen displayed on the user terminal 20 after step S132 of FIG.
  • the user terminal 20 displays an image obtained by cutting out the entire image processed by the image processing unit 120 or a region of the image including the unspecified article 40a.
  • This image contains information that can identify the unspecified article 40a (for example, a frame line surrounding the unspecified article 40a).
  • the user terminal 20 also displays an estimated product name for the unspecified article 40a.
  • the user of the user terminal 20 can confirm the image of the unspecified article 40a and the estimation result of the product name of the unspecified article 40a in association with each other.
  • the image processing unit 120 of the image processing device 10 specifies the product name of the article on the shelf by image processing. Then, when there is an unspecified article 40a that the image processing unit 120 could not identify, the article estimation unit 130 puts the product name of the unspecified article 40a next to the unspecified article 40a when the specific condition is satisfied. Estimating the product name of the located article 40 This makes it possible to estimate the product name of the article with high accuracy when there is an article that is not recognized by the image processing.
  • An image processing means for identifying each of the plurality of articles shown in the image by processing an image obtained by photographing a plurality of articles arranged on the shelf.
  • the product name of the unspecified article is changed to the product name of the article located next to the unspecified article.
  • Goods estimation means to estimate and An image processing device comprising.
  • the specific condition is an image processing apparatus in which the product names of the articles on both sides of the unspecified article are the same. 3. 3.
  • the specific condition is an image processing device in which the unspecified article is located at the edge of the shelf. 4.
  • the image shows the first shelf and the second shelf one above the first shelf.
  • the specific condition is the lateral direction of the first unspecified article arranged on the first shelf and the second unspecified article arranged on the second shelf.
  • An image processing device in which the difference between the positions of is equal to or less than the reference value. 5.
  • the specific condition is an image processing apparatus in which the arrangement of product names of the plurality of articles has regularity, and the unspecified article has the same product name as the adjacent article in the regularity. 6.
  • the computer By processing images of a plurality of articles placed on the shelves, the product names of the plurality of articles shown in the images can be identified.
  • Image processing that estimates the product name of the unspecified article as the product name of the article located next to the unspecified article when the specific conditions are satisfied for the unspecified article that cannot be specified.
  • Method. 7 In the image processing method described in 6 above, The specific condition is an image processing method in which the product names of the articles on both sides of the unspecified article are the same. 8. In the image processing method described in 6 above, The specific condition is an image processing method in which the unspecified article is located at the edge of the shelf. 9. In the image processing method described in 6 above, The image shows the first shelf and the second shelf one above the first shelf.
  • the specific condition is the lateral direction of the first unspecified article arranged on the first shelf and the second unspecified article arranged on the second shelf.
  • An image processing method in which the difference between the positions of is less than or equal to the reference value. 10.
  • the specific condition is an image processing method in which the arrangement of product names of the plurality of articles has regularity, and the unspecified article has the same product name as the adjacent article in the regularity. 11.
  • On the computer A function of identifying the product name of each of the plurality of articles shown in the image by processing an image of a plurality of articles arranged on the shelf, and a function of identifying the product name of each of the plurality of articles shown in the image.
  • the specific condition is a program in which the product names of the articles on both sides of the unspecified article are the same.
  • the specific condition is a program in which the unspecified article is located at the edge of the shelf.
  • the image shows the first shelf and the second shelf one above the first shelf.
  • the specific condition is the lateral direction of the first unspecified article arranged on the first shelf and the second unspecified article arranged on the second shelf.
  • the specific condition is a program in which the arrangement of product names of the plurality of articles has regularity, and the unspecified article has the same product name as the adjacent article in the regularity.
  • Image processing device 10
  • User terminal 30 Shelf 30a First shelf 30b Second shelf 32 Side surface 40
  • Article 40a Unspecified article 110 Acquisition unit 120 Image processing unit 130
  • Article estimation unit 140
  • Reference data storage unit 142
  • Result storage unit 150
  • Output unit

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PCT/JP2020/034551 2019-09-17 2020-09-11 画像処理装置、画像処理方法、及びプログラム Ceased WO2021054266A1 (ja)

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JP2021546649A JP7613360B2 (ja) 2019-09-17 2020-09-11 画像処理装置、画像処理方法、及びプログラム
US17/641,045 US12211247B2 (en) 2019-09-17 2020-09-11 Image processing apparatus, image processing method, and program
US18/219,498 US12299954B2 (en) 2019-09-17 2023-07-07 Image processing apparatus, image processing method, and program
US18/220,038 US12299955B2 (en) 2019-09-17 2023-07-10 Image processing apparatus, image processing method, and program
US19/173,956 US20250239046A1 (en) 2019-09-17 2025-04-09 Image processing apparatus, image processing method, and program
US19/174,004 US20250239047A1 (en) 2019-09-17 2025-04-09 Image processing apparatus, image processing method, and program

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US18/219,498 Continuation US12299954B2 (en) 2019-09-17 2023-07-07 Image processing apparatus, image processing method, and program
US18/220,038 Continuation US12299955B2 (en) 2019-09-17 2023-07-10 Image processing apparatus, image processing method, and program

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