US 12,394,239 B2
Pedestrian re-identification method and device
Lingxiao He, Beijing (CN); Boqiang Xu, Beijing (CN); Xingyu Liao, Beijing (CN); Wu Liu, Beijing (CN); Tao Mei, Beijing (CN); and Bowen Zhou, Beijing (CN)
Assigned to Beijing Jingdong Shangke Information Technology Co., Ltd., Beijing (CN); and Beijing Jingdong Century Trading Co., Ltd., Beijing (CN)
Appl. No. 18/013,795
Filed by Beijing Jingdong Shangke Information Technology Co., Ltd., Beijing (CN); and Beijing Jingdong Century Trading Co., Ltd., Beijing (CN)
PCT Filed May 7, 2021, PCT No. PCT/CN2021/092020
§ 371(c)(1), (2) Date Dec. 29, 2022,
PCT Pub. No. WO2022/041830, PCT Pub. Date Mar. 3, 2022.
Claims priority of application No. 202010863443.9 (CN), filed on Aug. 25, 2020.
Prior Publication US 2023/0334890 A1, Oct. 19, 2023
Int. Cl. G06V 10/42 (2022.01); G06V 10/44 (2022.01); G06V 10/70 (2022.01); G06V 10/75 (2022.01); G06V 40/10 (2022.01)
CPC G06V 40/10 (2022.01) [G06V 10/42 (2022.01); G06V 10/44 (2022.01); G06V 10/70 (2022.01); G06V 10/751 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for pedestrian re-identification, comprising:
collecting a target image set comprising at least two target images, wherein each target image comprises at least one person;
extracting, using respective pre-trained models, a global feature and a head-shoulder feature of each person in the each target image in the target image set, wherein the global feature is an overall appearance feature, and the head-shoulder feature is a feature of a head part and a shoulder part;
determining a representation feature of the each person in the each target image based on the global feature and the head-shoulder feature of the each person in the each target image; and
determining whether a given person in different target images is the same person by assessing similarity between the respective representation feature of the each person in the each target image;
wherein the determining a representation feature of the each person in the each target image based on the global feature and head-shoulder feature of the each person in the each target image comprises:
acquiring, for the each person in the each target image, a weight value of the global feature and a weight value of the head-shoulder feature that correspond to a reference identification feature in the global feature of the person, wherein the weight value of the global feature that corresponds to the reference identification feature is less than the weight value of the head-shoulder feature that corresponds to the reference identification feature, and the weight value of the global feature and the weight value of the head-shoulder feature that correspond to the reference identification feature vary with the global feature; and
connecting the weighted features of both the global feature and the head-shoulder feature of the person to obtain the representation feature of the person.