Leland Stanford Junior University

Toyota Research Institute, Inc.

Juan Carlos Niebles

Adrien David Gaidon

Ehsan Adeli-Mosabbeb

Kuan-hui Lee

Kathleen Poston

Ehsan Adeli Mosabbeb

Zhangjie Cao

Kuan Lee

Mandy Lu

Bingbin Liu

Nishant Rai

Systems and methods for multi-view cooperative contrastive self-supervised learning, may include receiving a plurality of video sequences, the video sequences comprising a plurality of image frames; applying selected images of a first and second video sequence of the plurality of video sequences to a plurality of different encoders to derive a plurality of embeddings for different views of the selected images of the first and second video sequences; determining distances of the derived plurality of embeddings for the selected images of the first and second video sequences; detecting inconsistencies in the determined distances; and predicting semantics of a future image based on the determined distances.

Cooperative-contrastive learning systems and methods

Many embodiments of the invention include systems and methods for evaluating motion from a video, the method includes identifying a target individual in a set of one or more frames in a video, analyzing the set of frames to determine a set of pose parameters, generating a 3D body mesh based on the pose parameters, identifying joint positions for the target individual in the set of frames based on the generated 3D body mesh, predicting a motion evaluation score based on the identified join positions, providing an output based on the motion evaluation score.

Systems and methods for estimation of Parkinson's Disease gait impairment severity from videos using MDS-UPDRS

A system and method for predicting pedestrian intent is provided. A prediction circuit comprising a plurality of gated recurrent units (GRUB) receives a sequence of images captured by a camera. The prediction circuit parses each frame of the sequence of images to identify one or more pedestrians and one or more objects. Using the parsed data, the prediction circuit generates a pedestrian-centric spatiotemporal graph, the parsed data comprising one or more identified pedestrians and one or more identified object. The prediction circuit uses the pedestrian-centric graph to determine a probability of one or more pedestrians crossing a street for each frame of the sequence of images.

Spatiotemporal relationship reasoning for pedestrian intent prediction

Growing community of inventors

Stanford, CA, United States of America

Juan Carlos Niebles