Real-time compressive tracking based on online feature selection

2014 ◽  
Vol 22 (3) ◽  
pp. 730-736 ◽  
Author(s):  
毛征 MAO Zheng ◽  
袁建建 YUAN Jian-jian ◽  
吴珍荣 WU Zhen-rong ◽  
曲劲松 QU Jin-song ◽  
李红岩 LI Hong-yan
Keyword(s):  
Feature Selection ◽  
Real Time ◽  
Compressive Tracking ◽  
Online Feature Selection

scholarly journals Optimization of Physical Activity Recognition for Real-Time Wearable Systems: Effect of Window Length, Sampling Frequency and Number of Features

2019 ◽  
Vol 9 (22) ◽  
pp. 4833 ◽  
Author(s):  
Ardo Allik ◽  
Kristjan Pilt ◽  
Deniss Karai ◽  
Ivo Fridolin ◽  
Mairo Leier ◽  
...  
Keyword(s):  
Physical Activity ◽  
Feature Selection ◽  
Real Time ◽  
Sampling Frequency ◽  
Classification Performance ◽  
Window Length ◽  
Decision Tree Classifier ◽  
Acceleration Signal ◽  
Wearable Systems ◽  
Tree Classifier

The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the acceleration signal, window length of the classification fragment, and the number of classification features, found with different feature selection methods. For parameter evaluation, a decision tree classifier was created based on the acceleration signals recorded during tests, where 25 healthy test subjects performed various physical activities. Overall average F1-score achieved in this study was about 0.90. Similar F1-scores were achieved with the evaluated window lengths of 5 s (0.92 ± 0.02) and 3 s (0.91 ± 0.02), while classification performance with 1 s were lower (0.87 ± 0.02). Tested sampling frequencies of 50 Hz, 25 Hz, and 13 Hz had similar results with most classified activity types, with an exception of outdoor cycling, where differences were significant. Using forward sequential feature selection enabled the decreasing of the number of features from initial 110 features to about 12 features without lowering the classification performance. The results of this study have been used for developing more efficient real-time physical activity classifiers.

scholarly journals Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM

2017 ◽  
Vol 7 (9) ◽  
pp. 884 ◽  
Author(s):  
Teng Wang ◽  
Juequan Chen ◽  
Xiangdong Gao ◽  
Yuxin Qin
Keyword(s):  
Feature Selection ◽  
Laser Welding ◽  
Real Time ◽  
Real Time Monitoring ◽  
Disk Laser

Online Feature Selection for Multi-Source Streaming Features

2022 ◽  
Author(s):  
Dianlong You ◽  
Miaomiao Sun ◽  
Shunpan Liang ◽  
Ruiqi Li ◽  
Yang Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Online Feature Selection ◽  
Selection For

Active Multi-view Object Recognition and Online Feature Selection

2017 ◽  
pp. 471-488 ◽  
Author(s):  
Christian Potthast ◽  
Andreas Breitenmoser ◽  
Fei Sha ◽  
Gaurav S. Sukhatme
Keyword(s):  
Feature Selection ◽  
Object Recognition ◽  
Online Feature Selection

scholarly journals Facial Expression Analysis by Machine Learning

2010 ◽  
pp. 239-258
Author(s):  
Siu-Yeung Cho ◽  
Teik-Toe Teoh ◽  
Yok-Yen Nguwi
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Classification Performance ◽  
Difficult Problem ◽  
Facial Features ◽  
Expression Recognition ◽  
Real Time Processing ◽  
Good Classification Performance

Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometimes, conventional classifiers are not even effective in handling those features and produce good classification performance. This chapter discusses the issues on how the advanced feature selection techniques together with good classifiers can play a vital important role of real-time facial expression recognition. Several feature selection methods and classifiers are discussed and their evaluations for real-time facial expression recognition are presented in this chapter. The content of this chapter is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.

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