Privileged Learning Using Regularization in the Problem of Evaluating the Human Posture-Reference-Cited by-同舟云学术

Privileged Learning Using Regularization in the Problem of Evaluating the Human Posture

Published:2023-07-01 Issue:4 Volume: Page:121-124
ISSN:0002-3388
Container-title:Известия Российской академии наук. Теория и системы управления
language:
Short-container-title:Teoriâ i sistemy upravleniâ

Author:

Kaprielova M. S.¹,Neichev R. G.²,Tikhonova A. D.²

Affiliation:

1. Federal Research Center “Computer Science and Control,” Russian Academy of Sciences, 119333, Moscow, Russia

2. Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Moscow oblast, Russia

Abstract

The problem of evaluating a person’s posture from video data is solved. Various key points of the human body are analyzed. We study the change in the accuracy of a fixed model when using different proportions in the regularization term of the loss function. It is shown that for a fixed number of training epochs, the accuracy of the model differs depending on the selected proportions. In addition, it is shown that the linear correlation between the trajectories of the key points that are part of the regularization term is not the main criterion for predicting the effectiveness of applying the regularization term of the loss function.

Publisher

The Russian Academy of Sciences

Reference10 articles.

1. Vapnik V., Vashist A. A New Learning Paradigm: Learning Using Privileged Information // Neural Networks. 2009. V. 22. P. 544–557.

2. Lehrmann A., Gehler P., Nowozin S. A Non-parametric Bayesian Network Prior of Human Pose // Proc. IEEE Intern. Conf. On Computer Vision. Sydney, 2013. P. 1281–1288.

3. Ionescu C., Papava D., Olaru V., Sminchisescu C. Human3. 6m: Large Scale Datasets and Predictive Methods for 3d Human Sensing in Natural Environments // IEEE Trans. On Pattern Analysis And Machine Intelligence. 2013. V. 36. P. 1325–1339.

4. Ignatov A., Strijov, V. Human Activity Recognition Using Quasiperiodic Time Series Collected from a Single Tri-axial Accelerometer // Multimedia Tools And Applications. 2016. V. 75. P. 7257–7270.

5. Katrutsa A., Strijov V. Stress Test Procedure for Feature Selection Algorithms // Chemometrics And Intelligent Laboratory Systems. 2015. V. 142. P. 172–183.