Patents by Inventor Cordelia Schmid

Cordelia Schmid has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • System and method for training a convolutional neural network and classifying an action performed by a subject in a video using the trained convolutional neural network
    Patent number: 10867184
    Abstract: A method for training a convolutional neural network for classification of actions performed by subjects in a video is realized by (a) for each frame of the video, for each key point of the subject, generating a heat map of the key point representing a position estimation of the key point within the frame; (b) colorizing each heat map as a function of the relative time of the corresponding frame in the video; (c) for each key point, aggregating all the colorized heat maps of the key point into at least one image representing the evolution of the position estimation of the key point during the video; and training the convolutional neural network using as input the sets associated to each training video of images representing the evolution of the position estimation of each key point during the video.
    Type: Grant
    Filed: March 20, 2019
    Date of Patent: December 15, 2020
    Inventors: Vasileios Choutas, Philippe Weinzaepfel, Jérôme Revaud, Cordelia Schmid
  • SYSTEM AND METHOD FOR TRAINING A CONVOLUTIONAL NEURAL NETWORK AND CLASSIFYING AN ACTION PERFORMED BY A SUBJECT IN A VIDEO USING THE TRAINED CONVOLUTIONAL NEURAL NETWORK
    Publication number: 20190303677
    Abstract: A method for training a convolutional neural network for classification of actions performed by subjects in a video is realized by (a) for each frame of the video, for each key point of the subject, generating a heat map of the key point representing a position estimation of the key point within the frame; (b) colorizing each heat map as a function of the relative time of the corresponding frame in the video; (c) for each key point, aggregating all the colorized heat maps of the key point into at least one image representing the evolution of the position estimation of the key point during the video; and training the convolutional neural network using as input the sets associated to each training video of images representing the evolution of the position estimation of each key point during the video.
    Type: Application
    Filed: March 20, 2019
    Publication date: October 3, 2019
    Applicant: Naver Corporation
    Inventors: Vasileios Choutas, Philippe Weinzaepfel, Jérôme Revaud, Cordelia Schmid