US 12,367,664 B2
Computer-readable recording medium storing label change program, label change method, and information processing apparatus
Yoshie Kimura, Kawasaki (JP); and Genta Suzuki, Kawasaki (JP)
Assigned to FUJITSU LIMITED, Kawasaki (JP)
Filed by Fujitsu Limited, Kawasaki (JP)
Filed on Oct. 3, 2022, as Appl. No. 17/959,156.
Claims priority of application No. 2021-194402 (JP), filed on Nov. 30, 2021.
Prior Publication US 2023/0169760 A1, Jun. 1, 2023
Int. Cl. G06F 18/23 (2023.01); G06V 10/25 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/40 (2022.01); G06V 20/52 (2022.01); G06V 40/10 (2022.01); G06V 40/20 (2022.01)
CPC G06V 10/7747 (2022.01) [G06V 10/25 (2022.01); G06V 10/763 (2022.01); G06V 20/41 (2022.01); G06V 40/20 (2022.01)] 7 Claims
OG exemplary drawing
 
1. A non-transitory computer-readable recording medium storing a label change program for causing a computer to execute a process comprising:
acquiring image data that includes a plurality of areas;
setting a label for each of the plurality of areas by inputting the image data to a first machine learning model;
specifying a behavior performed by a person located in a first area among the plurality of areas for an object located in a second area; and
changing a label set for the second area based on a specified behavior of the person,
wherein a process is executed including
setting each reference line that indicates a movement route of a person in an aisle region of the image data by using tracking information obtained by tracking the same person based on video data that includes the image data obtained by imaging an inside of a room,
specifying a position of each person that appears in the video data based on skeleton information of the each person,
specifying a movement trajectory of the each person in the video data by using a position of the each person,
generating a plurality of clusters by clustering based on a distance between the each reference line and a movement trajectory of the each person in the image data,
extracting a region of interest that includes a cluster for which an evaluation value based on an angle formed by each movement trajectory that belongs to the cluster and the reference line is equal to or larger than a threshold, for each of the plurality of clusters, and
changing the label set for each of the plurality of areas set by the first machine learning model based on a region of interest that includes the cluster.