US 12,260,675 B2
Person re-identification method, system and device, and computer-readable storage medium
Yaqian Zhao, Jiangsu (CN); Li Wang, Jiangsu (CN); and Baoyu Fan, Jiangsu (CN)
Assigned to Suzhou MetaBrain Intelligent Technology Co., Ltd., Suzhou (CN)
Appl. No. 18/718,411
Filed by SUZHOU METABRAIN INTELLIGENT TECHNOLOGY CO., LTD., Jiangsu (CN)
PCT Filed Sep. 19, 2022, PCT No. PCT/CN2022/119678
§ 371(c)(1), (2) Date Jun. 10, 2024,
PCT Pub. No. WO2023/206935, PCT Pub. Date Nov. 2, 2023.
Claims priority of application No. 202210469141.2 (CN), filed on Apr. 30, 2022.
Prior Publication US 2024/0420503 A1, Dec. 19, 2024
Int. Cl. G06V 40/16 (2022.01); G06T 7/13 (2017.01); G06V 10/25 (2022.01); G06V 10/75 (2022.01); G06V 10/771 (2022.01); G06V 20/70 (2022.01)
CPC G06V 40/173 (2022.01) [G06T 7/13 (2017.01); G06V 10/25 (2022.01); G06V 10/751 (2022.01); G06V 10/771 (2022.01); G06V 20/70 (2022.01); G06T 2207/20081 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A person re-identification method, comprising:
obtaining a first type of person image without a label;
making label information for the first type of person image;
training a target person re-identification network on a basis of the first type of person image and the label information to obtain a first trained target person re-identification network;
extracting a region of interest in the first type of person image; and
training, on a basis of the first type of person image and the region of interest, the first trained target person re-identification network to obtain a second trained target person re-identification network, and performing person re-identification on a basis of the target person re-identification network;
wherein the making label information for the first type of person image comprises:
determining body part boundary information in the first type of person image; and
taking the body part boundary information as the label information of the first type of person image;
wherein the extracting a region of interest in the first type of person image comprises:
extracting a feature map previous to an average pooling layer of the target person re-identification network;
superimposing all channels of the feature map to obtain Class Activation Mapping (CAM);
determining a real-time threshold value of the CAM on a basis of a hyper-parameter, and taking a pixel region in the first type of person image corresponding to the real-time threshold value greater than a preset threshold value as an initial region of interest;
searching a connected component in the initial region of interest, and determining the region of interest on a basis of the connected component; and
determining bounding box information of the region of interest.