scholarly journals Human motion classification using 2D stick-model matching regression coefficients

2016 ◽  
Vol 283 ◽  
pp. 70-89 ◽  
Author(s):  
C.K. Chan ◽  
W.P. Loh ◽  
I. Abd Rahim
Keyword(s):  
Human Motion ◽  
Regression Coefficients ◽  
Model Matching ◽  
Motion Classification ◽  
Stick Model ◽  
Human Motion Classification

scholarly journals Indoor non-rhythmic human motion classification using a frequency-modulated continuous-wave radar

2019 ◽  
Vol 2019 (20) ◽  
pp. 6928-6930
Author(s):  
Yu Zou ◽  
Chuanwei Ding ◽  
Hong Hong ◽  
Changzhi Li ◽  
Xiaohua Zhu
Keyword(s):  
Continuous Wave ◽  
Human Motion ◽  
Motion Classification ◽  
Wave Radar ◽  
Human Motion Classification

scholarly journals Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1264 ◽  
Author(s):  
Tomasz Hachaj
Keyword(s):  
Nearest Neighbor ◽  
Recognition Rate ◽  
Principal Component ◽  
Human Motion ◽  
Support Vector ◽  
Human Observer ◽  
Weak Classifier ◽  
Motion Classification ◽  
Observer Motion ◽  
Human Motion Classification

This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “weak classifier” (it does not randomly sample a feature set). The proposed classification method was evaluated on a challenging (even to a human observer) motion capture recording dataset of martial arts techniques performed by professional karate sportspeople. The dataset consisted of 360 recordings in 12 motion classes. Because some classes of these motions might be symmetrical (which means that they are performed with a dominant left or right hand/leg), an analysis was conducted to determine whether accounting for symmetry could improve the recognition rate of a classifier. The experimental results show that applying the proposed classifiers’ bagging procedure increased the recognition rate (RR) of the Nearest-Neighbor (NNg) and Support Vector Machine (SVM) classifiers by more than 5% and 3%, respectively. The RR of one trained classifier (SVM) was higher when we did not use symmetry. On the other hand, the application of symmetry information for bagged NNg improved its recognition rate compared with the results without symmetry information. We can conclude that symmetry information might be helpful in situations in which it is not possible to optimize the decision borders of the classifier (for example, when we do not have direct information about class labels). The experiment presented in this paper shows that, in this case, bagging and mirroring might help find a similar object in the training set that shares the same class label. Both the dataset that was used for the evaluation and the implementation of the proposed method can be downloaded, so the experiment is easily reproducible.

View-independent human motion classification using image-based reconstruction

2009 ◽  
Vol 27 (8) ◽  
pp. 1194-1206 ◽  
Author(s):  
R. Bodor ◽  
A. Drenner ◽  
D. Fehr ◽  
O. Masoud ◽  
N. Papanikolopoulos
Keyword(s):  
Human Motion ◽  
Motion Classification ◽  
Human Motion Classification
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