US 12,307,802 B2
Light weight multi-branch and multi-scale person re-identification
Hongzhi Tao, Beijing (CN); and Qiang Li, Beijing (CN)
Assigned to Intel Corporation, Santa Clara, CA (US)
Appl. No. 17/764,100
Filed by Intel Corporation, Santa Clara, CA (US)
PCT Filed Dec. 20, 2019, PCT No. PCT/CN2019/126906
§ 371(c)(1), (2) Date Mar. 25, 2022,
PCT Pub. No. WO2021/120157, PCT Pub. Date Jun. 24, 2021.
Prior Publication US 2022/0351535 A1, Nov. 3, 2022
Int. Cl. G06V 40/10 (2022.01); G06N 3/04 (2023.01); G06V 10/82 (2022.01); G06V 20/00 (2022.01); G06V 20/52 (2022.01)
CPC G06V 40/103 (2022.01) [G06N 3/04 (2013.01); G06V 10/82 (2022.01); G06V 20/38 (2022.01); G06V 20/52 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
interface circuitry;
machine-readable instructions; and
at least one processor circuit to be programmed by the machine-readable instructions to:
extract, using a first residual block of a convolutional neural network, a first local feature of a subject from one or more images of the subject input to the convolutional neural network, the first local feature associated with a first resolution;
extract, using a second residual block of the convolutional neural network, a second local feature of the subject from the one or more images, the second local feature associated with a second resolution, the second resolution different than the first resolution;
process, using a first number of refine blocks, the first local feature to generate a refined first local feature, the first number of refine blocks selected based on the first resolution;
process, using a second number of refine blocks, the second local feature to generate a refined second local feature, the second number of refine blocks selected based on the second resolution, the second number of refine blocks different than the first number of refine blocks;
generate final dynamic features for the subject based on the refined first local feature and the refined second local feature;
identify the subject in a first image and a second image using the final dynamic features, the first image different than the second image, the first image and the second image different from the one or more images of the subject input to the convolutional neural network; and
output an indicator identifying the subject in the first image and an indicator identifying the subject in the second image.