Sökning: "HRNet"

Hittade 3 uppsatser innehållade ordet HRNet.

  1. 1. Using transfer learning on 2D skeleton-based action recognition networks to improve performance on data from previously unseen camera angles

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Filip Cederquist; Gustav Edman Harrysson; [2021]
    Nyckelord :Mathematics and Statistics;

    Sammanfattning : Action recognition is a task in computer vision of inferring an action performed by a subject given an image or video. In this thesis we looked at how the performance of an action recognition network changed when introduced to new angles and how incorporating that data in the training affects this performance. LÄS MER

  3. 2. Visual assessments of Postural Orientation Errors using ensembles of Deep Neural Networks

    Master-uppsats, Lunds universitet/Matematisk statistik

    Författare :Filip Kronström; [2021]
    Nyckelord :Machine Learning; ACL; Anterior Cruciate Ligament; Deep Learning; Physiotherapy; Rehabilitation; Artificial Neural Network; video analysis; classification; Mathematics and Statistics;

    Sammanfattning : Injuries to the Anterior Cruciate Ligament (ACL) are severe and common among the physically active young to middle aged population. After suffering from such an injury, the patient typically face a lengthy rehabilitation process. Usually, it takes 1-2 years before an injured knee returns to pre-injury performance, if that is ever achieved. LÄS MER

  4. 3. Deep Learning for Point Detection in Images

    Master-uppsats, Linköpings universitet/Datorseende

    Författare :Björn Runow; [2020]
    Nyckelord :Deep learning; Point detection; U-Net; UNet ; HRNet; FCN; Weighted Hausdorff distance; EU-pallet; Computer vision;

    Sammanfattning : The main result of this thesis is a deep learning model named BearNet, which can be trained to detect an arbitrary amount of objects as a set of points. The model is trained using the Weighted Hausdorff distance as loss function. BearNet has been applied and tested on two problems from the industry. LÄS MER