Nvidia Corporation

Edwin Weill

Farzin Aghdasi

Parthasarathy Sriram

Fnu Ratnesh Kumar

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.

Training neural networks for vehicle re-identification

In various examples, a neural network may be trained for use in vehicle re-identification tasks&#x2014;e.g., matching appearances and classifications of vehicles across frames&#x2014;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&#x2014;referred to herein as batch sample&#x2014;may be used to identify embeddings, during training, that are most meaningful for updating parameters of the neural network.

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Edwin Weill