Nvidia corporation (20250022092). TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION
TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION
Organization Name
Inventor(s)
Fnu Ratnesh Kumar of Campbell CA US
Farzin Aghdasi of East Paolo Alto CA US
Parthasarathy Sriram of Los Altos CA US
Edwin Weill of Mountain View CA US
TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION
This abstract first appeared for US patent application 20250022092 titled 'TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION
Original Abstract Submitted
in various examples, a neural network may be trained for use in vehicle re-identification tasks—e.g., matching appearances and classifications of vehicles across frames—in a camera network. the neural network may be trained to learn an embedding space such that embeddings corresponding to vehicles of the same identify are projected closer to one another within the embedding space, as compared to vehicles representing different identities. to accurately and efficiently learn the embedding space, the neural network may be trained using a contrastive loss function or a triplet loss function. in addition, to further improve accuracy and efficiency, a sampling technique—referred to herein as batch sample—may be used to identify embeddings, during training, that are most meaningful for updating parameters of the neural network.
