Bertec Corporation

Bartec Corporation

Emre Akbas

Necip Berme

Mohan Chandra Baro

Batuhan Karagoz

Ozhan Suat

Muhammed Salih Karagoz

Bedirhan Uguz

Muhammed Kocabas

Cameron Scott Hobson

Utku Aktas

A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving the one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a three dimensional pose estimation neural network; (iii) generating, by using the three dimensional pose estimation neural network, vertices of a canonical human mesh model for the one or more images of the scene; (iv) retrieving a weight matrix from an annotation server that corresponds to a desired keypoint set; and (v) generating the desired keypoint set by multiplying the retrieved weight matrix with the vertices of the canonical human mesh model.

System for estimating a three dimensional pose of one or more persons in a scene

A system for training neural networks that predict the parameters of a human mesh model is disclosed herein. The system includes at least one camera and a data processor configured to execute computer executable instructions for: receiving a first frame and a second frame of a video from the at least one camera; extracting first and second image data from the first and second frames of the video; inputting the sequence of frames of the video into a human mesh estimator module, the human mesh estimator module estimating mesh parameters from the sequence of frames of the video so as to determine a predicted mesh; and generating a training signal for input into the human mesh estimator module by using a two-dimensional keypoint loss module that compares a first set of two-dimensional image-based keypoints to a second set of two-dimensional model-based keypoints.

System for training neural networks that predict the parameters of a human mesh model using dense depth and part-based UV map annotations

A system for training neural networks that predict the parameters of a human mesh model is disclosed herein. The system includes at least one camera and a data processor configured to execute computer executable instructions for: receiving a first frame and a second frame of a video from the at least one camera; extracting first and second features from the first and second frames of the video; inputting the sequence of frames of the video into a human mesh estimator module, the human mesh estimator module estimating mesh parameters from the sequence of frames of the video so as to determine a predicted mesh; and generating a training signal for input into the human mesh estimator module by using at least one of: (i) a depth loss module and (ii) a rigid transform loss module.

A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a first branch pose estimation neural network and a second branch pose estimation neural network; (iii) generating, by using a three dimensional reconstruction module, three dimensional reconstructions from two dimensional pose estimates produced by the second branch pose estimation neural network; and (iv) projecting, by using a reprojection module, the three dimensional reconstructions to camera image planes of respective image samples, and uploading the reprojections and image samples to an annotation server.

A system for estimating a pose of one or more persons in a scene includes a camera configured to capture one or more images of the scene; and a data processor configured to execute computer executable instructions for: (i) receiving the one or more images of the scene from the camera; (ii) extracting features from the one or more images of the scene for providing inputs to a keypoint subnet and a person detection subnet; (iii) generating one or more keypoints using the keypoint subnet; (iv) generating one or more person instances using the person detection subnet; (v) assigning the one or more keypoints to the one or more person instances by learning pose structures from image data; and (vi) determining one or more poses of the one or more persons in the scene using the assignment of the one or more keypoints to the one or more person instances.

System for estimating a pose of one or more persons in a scene

A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a first branch pose estimation neural network and second branch pose estimation neural network; (iii) generating a first training signal from the second branch pose estimation neural network using a three dimensional reconstruction module for input into the first branch pose estimation neural network; (iv) generating one or more volumetric heatmaps; and (v) applying a maximization function to the one or more volumetric heatmaps to obtain a 3D pose of one or more persons in the scene.

A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes at least one camera, the at least one camera configured to capture an image of the scene; and a data processor including at least one hardware component, the data processor configured to execute computer executable instructions. The computer executable instructions comprising instructions for: (i) receiving the image of the scene from the at least one camera; (ii) extracting features from the image of the scene for providing inputs to a convolutional neural network; (iii) generating one or more volumetric heatmaps using the convolutional neural network; and (iv) applying a maximization function to the one or more volumetric heatmaps to obtain a three dimensional pose of one or more persons in the scene.

A system for estimating a pose of one or more persons in a scene includes a camera configured to capture an image of the scene; and a data processor configured to execute computer executable instructions for: (i) receiving the image of the scene from the camera; (ii) extracting features from the image of the scene for providing inputs to a keypoint subnet and a person detection subnet; (iii) generating one or more keypoints using the keypoint subnet; (iv) generating one or more person instances using the person detection subnet; (v) assigning the one or more keypoints to the one or more person instances by learning pose structures from the image data; and (vi) determining one or more poses of the one or more persons in the scene using the assignment of the one or more keypoints to the one or more person instances.

A system for estimating a three dimensional pose and determining one or more biomechanical performance parameters of at least one person in a scene is disclosed herein. The system includes at least one camera and at least one measurement assembly, the at least one camera configured to capture an image of the scene; and a data processor including at least one hardware component, the data processor configured to execute computer executable instructions. The computer executable instructions comprising instructions for: (i) receiving the image of the scene from the at least one camera; (ii) extracting features from the image of the scene for providing inputs to a convolutional neural network; (iii) generating one or more volumetric heatmaps using the convolutional neural network; and (iv) applying a maximization function to the one or more volumetric heatmaps to obtain a three dimensional pose of the at least one person in the scene.

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Ankara, Turkey

Emre Akbas