Robust Recognition Methods

2010 ◽  
pp. 355-375
Author(s):  
Jean-Paul Haton
Keyword(s):  
Robust Recognition

scholarly journals Gravity Control-Based Data Augmentation Technique for Improving VR User Activity Recognition

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 845
Author(s):  
Dongheun Han ◽  
Chulwoo Lee ◽  
Hyeongyeop Kang
Keyword(s):  
Activity Recognition ◽  
Large Scale ◽  
Data Augmentation ◽  
Training Data ◽  
Measurement Unit ◽  
Gravitational Acceleration ◽  
The Neural Network ◽  
Typical Data ◽  
Robust Recognition ◽  
Gravity Acceleration

The neural-network-based human activity recognition (HAR) technique is being increasingly used for activity recognition in virtual reality (VR) users. The major issue of a such technique is the collection large-scale training datasets which are key for deriving a robust recognition model. However, collecting large-scale data is a costly and time-consuming process. Furthermore, increasing the number of activities to be classified will require a much larger number of training datasets. Since training the model with a sparse dataset can only provide limited features to recognition models, it can cause problems such as overfitting and suboptimal results. In this paper, we present a data augmentation technique named gravity control-based augmentation (GCDA) to alleviate the sparse data problem by generating new training data based on the existing data. The benefits of the symmetrical structure of the data are that it increased the number of data while preserving the properties of the data. The core concept of GCDA is two-fold: (1) decomposing the acceleration data obtained from the inertial measurement unit (IMU) into zero-gravity acceleration and gravitational acceleration, and augmenting them separately, and (2) exploiting gravity as a directional feature and controlling it to augment training datasets. Through the comparative evaluations, we validated that the application of GCDA to training datasets showed a larger improvement in classification accuracy (96.39%) compared to the typical data augmentation methods (92.29%) applied and those that did not apply the augmentation method (85.21%).

scholarly journals Robust Recognition and Segmentation of Human Actions Using HMMs with Missing Observations

2005 ◽  
Vol 2005 (13) ◽  
Author(s):  
Patrick Peursum ◽  
Hung H. Bui ◽  
Svetha Venkatesh ◽  
Geoff West
Keyword(s):  
Missing Observations ◽  
Human Actions ◽  
Robust Recognition

A Hybrid Deep Architecture for Robust Recognition of Text Lines of Degraded Printed Documents

2018 ◽  
Author(s):  
Chandan Biswas ◽  
Partha Sarathi Mukherjee ◽  
Koyel Ghosh ◽  
Ujjwal Bhattacharya ◽  
Swapan K. Parui
Keyword(s):  
Deep Architecture ◽  
Robust Recognition

scholarly journals Combining frame and turn-level information for robust recognition of emotions within speech

2007 ◽  
Author(s):  
Bogdan Vlasenko ◽  
Björn Schuller ◽  
Andreas Wendemuth ◽  
Gerhard Rigoll
Keyword(s):  
Level Information ◽  
Robust Recognition ◽  
Recognition Of Emotions

Robust Recognition of Indoor Educational Activity Based on Wi-Fi Signals with SSGAN

2021 ◽  
Vol 22 (7) ◽  
pp. 1543-1551
Author(s):  
Jian Zhao Jian Zhao ◽  
Xin Li Jian Zhao ◽  
Liang Huang Xin Li ◽  
Shangwu Chong Liang Huang ◽  
Jian Jia Shangwu Chong
Keyword(s):  
Educational Activity ◽  
Robust Recognition

Sign in / Sign up

Export Citation Format

Share Document