Patent ID: 11935302
Assignee: NANYANG TECHNOLOGICAL UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. A system for object re-identification, the system comprising:
a memory; and
at least one processor communicatively coupled to the memory and configured to perform operations comprising:
obtaining a first set of images from a first camera, and a second set of images from at least one second camera, wherein the at least one second camera is different from the first camera;
determining a first set of features based on the first set of images, the first set of features lying in a first feature space, wherein determining the first set of features based on the first set of images comprises:
generating first-level representations for the first set of images, wherein the first-level representations for the first set of images comprise image-level representations for the first set of images;
generating second-level representations for the first set of images, wherein the second-level representations for the first set of images comprise at least one of part-level representations for the first set of images or patch-level representations for the first set of images;
determining first-level features for the first set of images based on the first-level representations, wherein the first-level features for the first set of images comprise image-level features for the first set of images; and
determining second-level features for the first set of images based on the second-level representations, wherein the second-level features for the first set of images comprise at least one of part-level features for the first set of images or patch-level features for the first set of images, the first set of features comprising the first-level features for the first set of images and the second-level features for the first set of images;

determining a second set of features based on the second set of images, the second set of features lying in a second feature space, wherein determining the second set of features based on the second set of images comprises:
generating first-level representations for the second set of images, wherein the first-level representations for the second set of images comprise image-level representations for the second set of images;
generating second-level representations for the second set of images, wherein the second-level representations for the second set of images comprise at least one of part-level representations for the second set of images or patch-level representations for the second set of images;
determining first-level features for the second set of images based on the first level-representations, wherein the first-level features for the second set of images comprise image-level features for the second set of images; and
determining second-level features for the second set of images based on the second-level representations, wherein the second-level features for the second set of images comprise at least one of part-level features for the second set of images or patch-level features for the second set of images, the second set of features comprising the first-level features for the second set of images and the second-level features for the second set of images;

determining a first feature projection matrix and a second feature projection matrix that respectively map the first set of features and the second set of features to a shared feature space; and
determining a common dictionary based on the shared feature space, wherein the first set of features mapped to the shared feature space and the second set of features mapped to the shared feature space are represented by entries of the common dictionary.