US 12,118,469 B2
Person re-identification device and method
Bum Sub Ham, Seoul (KR); and Hyun Jong Park, Seoul (KR)
Assigned to INDUSTRY-ACADEMIC COOPERATION FOUNDATION, YONSEI UNIVERSITY, Seoul (KR)
Filed by INDUSTRY-ACADEMIC COOPERATION FOUNDATION, YONSEI UNIVERSITY, Seoul (KR)
Filed on Feb. 8, 2022, as Appl. No. 17/667,462.
Application 17/667,462 is a continuation of application No. PCT/KR2020/010753, filed on Aug. 13, 2020.
Claims priority of application No. 10-2019-0107457 (KR), filed on Aug. 30, 2019.
Prior Publication US 2022/0165048 A1, May 26, 2022
Int. Cl. G06T 7/00 (2017.01); G06N 3/084 (2023.01); G06V 10/44 (2022.01); G06V 10/50 (2022.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01); G06V 40/10 (2022.01)
CPC G06N 3/084 (2013.01) [G06T 7/97 (2017.01); G06V 10/454 (2022.01); G06V 10/50 (2022.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01); G06V 40/10 (2022.01); G06V 40/103 (2022.01); G06V 10/457 (2022.01)] 10 Claims
OG exemplary drawing
 
1. A person re-identification device for re-identifying a person included in an image, the device performing learning by receiving a plurality of learning images labeled with an identifier of a person included, and comprising:
a feature extracting and dividing unit, that receives a plurality of images including a person to be re-identified, extracts a feature of each image according to a pre-learned pattern estimation method to acquire a 3-dimensional feature vector, and divides the 3-dimensional feature vector into a pre-designated size unit to acquire a plurality of local feature vectors;
a one-to-many relational reasoning unit, that estimates a relationship between each of the plurality of local feature vectors and remaining local feature vectors according to a pre-learned pattern estimation method, and reflects the estimated relationship to each of the plurality of local feature vectors to acquire a plurality of local relational features;
a global contrastive pooling unit, that acquires a global contrastive feature by performing global contrastive pooling in which a relationship between a maximum feature and an average feature of the plurality of local feature vectors is reflected back to the maximum feature according to a pre-learned pattern estimation method; and
a person re-identification unit, that receives the plurality of local relational features and the global contrastive feature as a final descriptor of a corresponding image, and compares the final descriptor with a reference descriptor that is a final descriptor acquired in advance from an image including a person to be searched, thereby determining whether a person to be searched is included.