Patent ID: 11922320
Assignee: FORD GLOBAL TECHNOLOGIES, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A method, comprising:
training a dual variational autoencoder-generative adversarial network (VAE-GAN) to transform a real video sequence and a simulated video sequence by inputting the real video sequence into a real video decoder and a real video encoder and inputting the simulated video sequence into a synthetic video encoder and a synthetic video decoder;
determining real loss functions and synthetic loss functions based on output from a real video discriminator and a synthetic video discriminator, respectively, wherein the real video discriminator and the synthetic video discriminator rearrange the video sequences to stack the video frames along dimensions corresponding to the RGB color channels and wherein the real video discriminator and the synthetic video discriminator are trained using loss functions determined based on real video sequences and synthetic video sequences;
backpropagating the real loss functions through the real video encoder and the real video decoder to train the real video encoder and the real video decoder based on the real loss functions;
backpropagating the synthetic loss functions through the synthetic video encoder and the synthetic video decoder to train the synthetic video encoder and the synthetic video decoder based on the synthetic loss functions;
training the real video discriminator and the synthetic video discriminator to determine an authentic video sequence from a fake video sequence using the real loss functions and the synthetic loss functions;
transforming an annotated simulated video sequence with the synthetic video encoder and the real video decoder of the dual VAE-GAN to generate an annotated reconstructed real video sequence that includes style elements based on the real video sequence; and
training a deep neural network using the reconstructed annotated real video sequence to detect and track objects in video data.