# US 11,308,358 B2 · Method and system for automatically annotating and identifying a living being or an object with an identifier providing a subject identification > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,308,358 B2 - Document ID: US11308358B2 - Status: Granted - Kind: B2 - Application number: 17/042,063 - Filed: 2019-08-15 - Published: 2021-05-13 - Granted: 2022-04-19 - Earliest claimed: 2018-08-17 - Assignees: Kepler Vision Technologies B.V. - Applicants: Kepler Vision Technologies B.V. - Inventors: Marc Jean Baptist Van Oldenborgh; Cornelis Gerardus Maria Snoek - Web page: https://patentgrep.com/patent/US11308358B2 ## CPC - G06V 40/103: {Static body considered as a whole, e.g. static pedestrian or occupant recognition} - G06F 18/2148: {characterised by the process organisation or structure, e.g. boosting cascade} - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06K 7/0021: {for reading/sensing record carriers having surface contacts} - G06K 7/10366: {the interrogation device being adapted for miscellaneous applications} - G06K 7/10722: {Photodetector array or CCD scanning} - G06K 7/1413: {1D bar codes} - G06N 20/00: Machine learning - G06V 10/225: {based on a marking or identifier characterising the area} - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06V 20/53: {Recognition of crowd images, e.g. recognition of crowd congestion} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06K: GRAPHICAL DATA READING (image or video recognition or understanding G06V); PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract The invention relates to a method for training a machine learning model to identify a subject having at least one machine readable identifier providing a subject ID, said method comprising: providing a computer vision system with an image capturing system comprising at least one image capturing device, and a reader system comprising at least one reader for reading said at least one machine readable identifier; defining said machine learning model in said computer vision system; capturing a first image using said image capturing system, said first image showing said subject; reading said subject ID using said reader system when capturing said first image, and linking said subject ID with said first image, said linking providing said first image with a linked subject ID, providing a first annotated image; capturing at least one further image showing said subject, linking said linked subject ID to said at least one further image providing at least one further annotated image, and subjecting said first annotated image and said at least one further annotated image to said machine learning model for training said machine learning model. ## Claims ### Claim 1 A method for training a machine learning model to identify a subject having at least one machine readable identifier providing a subject ID, said method comprising: providing a computer vision system with an image capturing system comprising at least one image capturing device, and a reader system comprising at least one reader for reading said at least one machine readable identifier; defining said machine learning model in said computer vision system; capturing a first image using said image capturing system, said first image showing said subject; reading said subject ID using said reader system when capturing said first image, and linking said subject ID with said first image, said linking providing said first image with a linked subject ID, providing a first annotated image; capturing at least one further image showing said subject, linking said linked subject ID to said at least one further image providing at least one further annotated image, and subjecting said first annotated image and said at least one further annotated image to said machine learning model for training said machine learning model. ### Claim 2 (depends on 1) The method of claim 1, further comprising providing said subject with said machine readable identifier providing said subject ID. ### Claim 3 (depends on 1) The method of claim 1, wherein when capturing said at least one further image, a further subject ID is read using said reader system and said further subject ID is linked to said at least one further image. ### Claim 4 (depends on 1) The method of claim 1, wherein said first annotated image and said at least one further annotated image are included in a training dataset that is built during performing said method, and said training dataset is used for at least one of training and additionally training said machine learning model. ### Claim 5 (depends on 1) The method of claim 1, wherein said machine learning model comprises a machine learning model part for localizing subjects in at least one of said captured first image and said captured at least one further image. ### Claim 6 (depends on 1) The method of claim 1, wherein said reader system comprises at least a first reader and a second reader, wherein said first reader reads said subject ID when said first image is captured, and said second reader reads said subject ID when said at least one further image is captured. ### Claim 7 (depends on 6) The method of claim 6, wherein said subject comprises at least a first and a second machine readable identifier first reader reads said first machine readable identifier for providing said subject ID and said second reader reads said second machine readable identifier for providing said subject ID. ### Claim 8 (depends on 1) The method of claim 1, wherein said first and second reader and said first and a second machine readable identifier are of a different type, wherein said first and second reader provide a first and second identifier, and in particular said vision system provides said subject ID from said first and second identifier. ### Claim 9 (depends on 1) The method of claim 1, wherein reading at least one selected from said linked subject ID and a further subject ID is repeated. ### Claim 10 (depends on 1) The method of claim 1, wherein said capturing said at least one further image and said linking said linked subject ID to said at least one further image continuously repeated, providing a series of further annotated images comprising at least said further annotated image, in particular said capturing is repeated when there is one or more subject in a field of view of said image capturing system. ### Claim 11 (depends on 1) The method of claim 1, wherein said capturing said at least one further image is continuously repeated, and said reader system repeats reading said subject ID using said reader system when said at least one further image is captured, providing each time a renewed subject ID, linking said renewed subject ID with said at least one further image, said linking providing said at least one further image with a linked subject ID, resulting in at least one further annotated image, for providing a series of annotated images. ### Claim 12 (depends on 1) The method of claim 1, wherein said annotating images is continued until a predetermined reliability level for identifying said subject in an image is reached. ### Claim 13 (depends on 1) The method of claim 1, for training a machine learning model to identify a plurality of said subjects each having at least one machine readable identifier providing a subject ID for each subject, wherein said reader system reads said machine readable identifiers of at least part of said plurality of subjects, providing a series of subject IDs, said image capturing system captures said first image with said at least part of said plurality of subjects and, and links said first image with said at least part of said plurality of subjects with said series of subject IDs, providing said first annotated image. ### Claim 14 (depends on 13) The method of claim 13, wherein said image capturing system captures said at least one further image with said at least part of said plurality of subjects, and links said at least one further image with said at least part of said plurality of subjects with said series of subject IDs, providing said at least one further annotated image. ### Claim 15 (depends on 1) A method for training a machine learning model to identify an animal among a group of animals, in particular a livestock animal amidst a group of livestock animals, using the method according to claim 1. ### Claim 16 A system for identifying a subject having at least one machine readable identifier providing a subject ID, said system comprising: a computer vision system comprising an image capturing system comprising at least one image capturing device, and a reader system comprising at least one reader for reading said at least one machine readable identifier; a machine learning model defined in said computer vision system; said computer vision system in operation: capturing a first image using said image capturing system, said image showing said subject; reading said subject ID using said reader system when capturing said first image, and linking said subject ID with said first image, said linking providing said first image with a linked subject ID, providing a first annotated image; capturing at least one further image showing said subject, linking said linked subject ID to said at least one further image providing at least one further annotated image, and subjecting said first annotated image and said at least one further annotated image to said machine learning model for training said machine learning model. ### Claim 17 (depends on 1) A non-transitory computer readable medium having stored thereon computer program instructions that, when executed by a data processor on a computer vision system enables said computer vision system to perform the method of claim 1.