US 12,307,742 B2
Person re-identification method, system, and device, and computer readable storage medium
Runze Zhang, Jiangsu (CN); Liang Jin, Jiangsu (CN); and Zhenhua Guo, Jiangsu (CN)
Assigned to INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD., Jiangsu (TW)
Appl. No. 17/914,799
Filed by INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD., Jiangsu (CN)
PCT Filed Sep. 24, 2020, PCT No. PCT/CN2020/117333
§ 371(c)(1), (2) Date Sep. 27, 2022,
PCT Pub. No. WO2021/212737, PCT Pub. Date Oct. 28, 2021.
Claims priority of application No. 202010327772.1 (CN), filed on Apr. 23, 2020.
Prior Publication US 2024/0005633 A1, Jan. 4, 2024
Int. Cl. G06V 10/764 (2022.01); G06V 40/10 (2022.01)
CPC G06V 10/764 (2022.01) [G06V 40/10 (2022.01)] 14 Claims
OG exemplary drawing
 
1. A person Re-identification (Re-ID) method, comprising:
acquiring a sample set to be trained;
training a pre-constructed person Re-ID model by a data re-sampling method and a cross-validation method based on the sample set to be trained to obtain a trained person Re-ID model; and
performing person Re-ID based on the trained person Re-ID model;
wherein persons in any two groups are of different classes after the sample set to be trained is grouped according to the cross-validation method;
wherein the training the pre-constructed person Re-ID model by the data re-sampling method and the cross-validation method based on the sample set to be trained to obtain the trained person Re-ID model comprises:
determining a hyperparameter type, hyperparameter search space, and hyperparameter adjustment priority of the pre-constructed person Re-ID model, wherein the hyperparameter type comprises an image number of each person, a boundary of image number, an initial learning rate, a training cycle, a triplet loss threshold, and a Rerank parameter set; and
training the pre-constructed person Re-ID model based on the hyperparameter type, the hyperparameter search space, and the hyperparameter adjustment priority;
wherein a hyperparameter search space of the image number of each person comprises {2, 4, 8}; a hyperparameter search space of the boundary of image number comprises {10, 30, 50, 70, 100}; a hyperparameter search space of the initial learning rate comprises {0.00035, 0.001, 0.003, 0.01}; a hyperparameter search space of the training cycle comprises {80, 120, 160, 240}; a hyperparameter search space of the triplet loss threshold comprises {0.3, 1.2, 4.8, 10.0, 20.0}; a hyperparameter search space of k1 in the Rerank parameter set comprises {1, 5, 10, 15, 20}; a hyperparameter search space of k2 in the Rerank parameter set comprises {1, 2, 3, 4, 5, 6}; and a hyperparameter search space of λ in the Rerank parameter set comprises {0.3, 0.6, 0.9}; and
wherein a sequence of the hyperparameter adjustment priority from high to low comprises: the training cycle, the initial learning rate, the image number of each person, the triplet loss threshold, the boundary of image number, k1, k2, and λ.