# US 2025/0095162 A1 · Learning apparatus, collation apparatus, learning method, and collation method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095162 A1 - Document ID: US20250095162A1 - Status: Published application - Kind: A1 - Application number: 18/832,545 - Filed: 2022-02-10 - Published: 2025-03-20 - Earliest claimed: 2022-02-10 - Assignees: NEC Corporation - Applicants: NEC Corporation - Inventors: Satoshi YAMAZAKI - Web page: https://patentgrep.com/patent/US20250095162A1 ## CPC - G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) - G06F 16/73: Querying - G06T 7/00: Image analysis - G06V 10/44: Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components - G06V 10/761: {Proximity, similarity or dissimilarity measures} - G06V 10/762: using clustering, e.g. of similar faces in social networks - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A ground truth weight generation unit generates a ground truth weight for each piece of tracking object data of tracking object information regarding a tracking object by using ground truth tracking object pair information that is a set of tracking object information of the same tracking object or a set of tracking object information of separate tracking objects. An inference model training unit trains, by machine learning, an inference model that outputs a tracking object data weight corresponding to tracking object data included in the tracking object information by using data regarding the tracking object information as input data and using a ground truth weight generated for the tracking object information as ground truth data. ## Claims ### Claim 1 A learning apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: generate, for each piece of tracking object data of tracking object information including at least feature amount information indicating a feature of a tracking object that is an object to be tracked and including one or more pieces of tracking object data obtained by tracking the tracking object with a video, a ground truth weight corresponding to ground truth data of a tracking object data weight regarding a degree of importance indicating how well the tracking object data represents the feature of the corresponding tracking object in the tracking object information by using ground truth tracking object pair information that is a set of the tracking object information of the same tracking object or a set of the tracking object information of separate tracking objects; train, by machine learning, an inference model configured to output a tracking object data weight corresponding to tracking object data included in the tracking object information by using data regarding the tracking object information as input data and using the ground truth weight generated for the tracking object information as ground truth data; and generate the tracking object data weight to be used in association with similarity between tracking object data included in the tracking object information regarding a first tracking object of a pair of tracking objects and tracking object data included in the tracking object information regarding a second tracking object when calculating a tracking object collation score that is a collation score of the pair of tracking objects in collation processing of the pair of tracking objects. ### Claim 2 (depends on 1) The learning apparatus according to claim 1, wherein the at least one processor is further configured to execute the instructions to generate a ground truth weight regarding the tracking object data based on similarity between each piece of the tracking object data included in the tracking object information of one tracking object and each piece of the tracking object data included in the tracking object information of the other tracking object in each of a plurality of pieces of the ground truth tracking object pair information. ### Claim 3 (depends on 2) The learning apparatus according to claim 2, wherein the at least one processor is further configured to execute the instructions to assign a point to the tracking object data based on the calculated similarity, and generate a ground truth weight regarding the tracking object data in correspondence with a number of assigned points. ### Claim 4 (depends on 3) The learning apparatus according to claim 3, wherein the at least one processor is further configured to execute the instructions to assign a point to the tracking object data corresponding to highest similarity among similarities calculated by using the set of tracking object information of same tracking object among the plurality of pieces of ground truth tracking object pair information. ### Claim 5 (depends on 3) The learning apparatus according to claim 3, wherein the at least one processor is further configured to execute the instructions to assign a point to the tracking object data corresponding to lowest similarity among similarities calculated by using the set of the tracking object information of separate tracking objects among the plurality of pieces of ground truth tracking object pair information. ### Claim 6 (depends on 1) The learning apparatus according to claim 1, wherein the at least one processor is further configured to execute the instructions to generate pseudo ground truth tracking object pair information that is a set of the tracking object information of tracking objects considered as being identical to each other or a set of the tracking object information of tracking objects considered as being separate from each other by using one or more pieces of tracking object cluster information obtained by clustering the tracking object information regarding a plurality of tracking objects considered as being identical to each other, and generate the ground truth weight by using the pseudo ground truth tracking object pair information as the ground truth tracking object pair information. ### Claim 7 (depends on 6) The learning apparatus according to claim 6, wherein the at least one processor is further configured to execute the instructions to generate the pseudo ground truth tracking object pair information that is a set of the tracking object information of tracking objects considered as being identical to each other by using the tracking object cluster information including the tracking object information regarding a predetermined number or more of tracking objects. ### Claim 8 (depends on 6) The learning apparatus according to claim 6, wherein the at least one processor is further configured to execute the instructions to generate pseudo ground truth tracking object pair information that is a set of the tracking object information of tracking objects considered as being separate from each other by using a set of first tracking object cluster information and second tracking object cluster information different from the first tracking object cluster information such that a maximum value of a collation score calculated between each piece of the tracking object information corresponding to the first tracking object cluster information and each piece of the tracking object information included in the second tracking object cluster information is equal to or less than a predetermined threshold value. ### Claim 9 (depends on 1) The learning apparatus according to claim 1, wherein the at least one processor is further configured to execute the instructions to designate an element of the input data input to the inference model. ### Claim 10 (depends on 1) The learning apparatus according to claim 1, wherein the at least one processor is further configured to execute the instructions to train the inference model by using at least graph structure data indicating a similarity relationship between a plurality of pieces of the tracking object data included in the tracking object information as input data. ### Claim 11 A collation apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: infer a tracking object data weight corresponding to each piece of tracking object data included in tracking object information of each of a pair of tracking objects to be collated by using an inference model trained in advance by machine learning, the inference model being trained to output the tracking object data weight corresponding to tracking object data included in the tracking object information regarding input data by using, as the input data, data regarding tracking object information including at least feature amount information indicating a feature of the tracking object that is an object to be tracked and including one or more pieces of tracking object data obtained by tracking the tracking object with a video and by using a ground truth weight, as ground truth data, corresponding to ground truth data of a tracking object data weight regarding a degree of importance indicating how well the tracking object data indicates a feature of the corresponding tracking object in the tracking object information; and perform collation processing of the pair of tracking objects by calculating a tracking object collation score that is a collation score of the pair of tracking objects by associating similarity between tracking object data included in the tracking object information regarding a first tracking object of the pair of tracking objects and tracking object data included in the tracking object information regarding a second tracking object with the inferred tracking object data weight. ### Claim 12 (depends on 11) The collation apparatus according to claim 11, wherein the at least one processor is further configured to execute the instructions to use at least graph structure data indicating a similarity relationship between a plurality of pieces of the tracking object data included in the tracking object information as the input data to infer the tracking object data weight by using the inference model. ### Claim 13 A learning method comprising: generating, for each piece of tracking object data of tracking object information including at least feature amount information indicating a feature of a tracking object that is an object to be tracked and including one or more pieces of tracking object data obtained by tracking the tracking object with a video, a ground truth weight corresponding to ground truth data of a tracking object data weight regarding a degree of importance indicating how well the tracking object data represents the feature of the corresponding tracking object in the tracking object information by using ground truth tracking object pair information that is a set of the tracking object information of the same tracking object or a set of the tracking object information of separate tracking objects; and training, by machine learning, an inference model configured to output a tracking object data weight corresponding to tracking object data included in the tracking object information by using data regarding the tracking object information as input data and using the ground truth weight generated for the tracking object information as ground truth data, wherein the tracking object data weight is used in association with similarity between tracking object data included in the tracking object information regarding a first tracking object of a pair of tracking objects and tracking object data included in the tracking object information regarding a second tracking object when calculating a tracking object collation score that is a collation score of the pair of tracking objects in collation processing of the pair of tracking objects. ### Claim 14 (depends on 13) The learning method according to claim 13, wherein a ground truth weight regarding the tracking object data is generated based on similarity between each piece of the tracking object data included in the tracking object information of one tracking object and each piece of the tracking object data included in the tracking object information of the other tracking object in each of a plurality of pieces of the ground truth tracking object pair information. ### Claim 15 (depends on 14) The learning method according to claim 14, wherein a point is assigned to the tracking object data based on the calculated similarity, and a ground truth weight regarding the tracking object data is generated in correspondence with a number of assigned points. ### Claim 16 (depends on 15) The learning method according to claim 15, wherein a point is assigned to the tracking object data corresponding to highest similarity among similarities calculated by using the set of tracking object information of same tracking object among the plurality of pieces of ground truth tracking object pair information. ### Claim 17 (depends on 15) The learning method according to claim 15, wherein a point is assigned to the tracking object data corresponding to lowest similarity among similarities calculated by using the set of tracking object information of the separate tracking objects among the plurality of pieces of ground truth tracking object pair information. ### Claim 18 (depends on 13) The learning method according to claim 13, further comprising: generating pseudo ground truth tracking object pair information that is a set of the tracking object information of tracking objects considered as being identical to each other or a set of the tracking object information of tracking objects considered as being separate from each other by using one or more pieces of tracking object cluster information obtained by clustering the tracking object information regarding a plurality of tracking objects considered as being identical to each other; and generating the ground truth weight by using the pseudo ground truth tracking object pair information as the ground truth tracking object pair information. ### Claim 19 (depends on 18) The learning method according to claim 18, wherein the pseudo ground truth tracking object pair information that is a set of the tracking object information of tracking objects considered as being identical to each other is generated by using the tracking object cluster information including the tracking object information regarding a predetermined number or more of tracking objects. ### Claim 20 (depends on 18) The learning method according to claim 18, wherein the pseudo ground truth tracking object pair information that is a set of the tracking object information of tracking objects considered as being separate from each other is generated by using a set of first tracking object cluster information and second tracking object cluster information different from the first tracking object cluster information such that a maximum value of a collation score calculated between each piece of the tracking object information corresponding to the first tracking object cluster information and each piece of the tracking object information included in the second tracking object cluster information is equal to or less than a predetermined threshold value. ### Claim at position 21 21-26. (canceled)