Classifying Human Body Postures by a Support Vector Machine with Two Simple Features

被引：0

作者：

Nguyen Van Tao ^{[1
]}

Nong Thi Hoa ^{[1
]}

Quach Xuan Truong ^{[1
]}

机构：

[1] Thai Nguyen Univ Informat & Commun Technol, Quyet Thang, Thai Nguyen, Vietnam

来源：

ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY | 2017年 / 538卷

关键词：

Posture classification; Human behaviour analysis; Support Vector Machine; RECOGNITION;

D O I：

10.1007/978-3-319-49073-1_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Human behaviour analysis helps to monitor a person's daily activities and detect home care emergencies. Classifying posture is an important step of human behaviour analysis. Many studies improve the accuracy of classifying. However, the number of features is big or extracting these features uses complicated formulas. Therefore, we proposed two features with simple computing. Two new features are formulated from the height and the square showing the human body's silhouettes. Then, we choose a non-linear Support Vector Machine to classify postures based on proposed features. Experiments show Support Vector Machine classify effectively and better than other methods.

引用

页码：188 / 196

页数：9

共 50 条

[41] Simple Recurrent Neural Networks for Support Vector Machine Training
Sifa, Rafet
Paurat, Daniel
Trabold, Daniel
Bauckhage, Christian
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III, 2018, 11141 : 13 - 22
[42] Simple Features for R: Standardized Support for Spatial Vector Data
Pebesma, Edzer
R JOURNAL, 2018, 10 (01): : 439 - 446
[43] Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition
Tang, Chao
Tong, Anyang
Zheng, Aihua
Peng, Hua
Li, Wei
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
[44] SUPPORT VECTOR MACHINE BASED AUTOMATIC CLASSIFICATION OF HUMAN BRAIN USING MR IMAGE FEATURES
Selvaraj, Henry
Selvi, S. Thamarai
Selvathi, D.
Ramkumar, R.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2006, 6 (03) : 357 - 370
[45] Classifying burst and suppression in the EEG of post asphyctic newborns using a Support Vector Machine
Lofhede, J.
Lofgren, N.
Thordstein, M.
Flisberg, A.
Kjellmer, I.
Lindecrantz, K.
2007 3RD INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, VOLS 1 AND 2, 2007, : 634 - +
[46] A new sampling method for classifying imbalanced data based on support vector machine ensemble
Jian, Chuanxia
Gao, Jian
Ao, Yinhui
NEUROCOMPUTING, 2016, 193 : 115 - 122
[47] AUTOMATIC CLASSIFYING METHOD FOR WEDGE TIGHTNESS BY SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORK
Poombansao, Thanachai
Kongprawechnon, Waree
Kittipiyakul, Somsak
Theeraworn, Chonlada
Charoenlarp, Muntita
Chunsangnate, It
SURANAREE JOURNAL OF SCIENCE AND TECHNOLOGY, 2015, 22 (01): : 5 - 14
[48] Support vector machine: Classifying and predicting mutagenicity of complex mixtures based on pollution profiles
Zheng, Weiwei
Tian, Dajun
Wang, Xia
Tian, Weidong
Zhang, Hao
Jiang, Songhui
He, Gengsheng
Zheng, Yuxin
Qu, Weidong
TOXICOLOGY, 2013, 313 (2-3) : 151 - 159
[49] Classifying Structured Web Sources Using Support Vector Machine and Aggressive Feature Selection
Le, Hieu Quang
Conrad, Stefan
WEB INFORMATION SYSTEMS AND TECHNOLOGIES, 2010, 45 : 270 - 282
[50] Classifying primary progressive aphasias individually with support vector machine approaches in MRI data
Bisenius, S.
Mueller, K.
Diehl-Schmid, J.
Fassbender, K.
Jessen, F.
Kassubek, J.
Kornhuber, J.
Schneider, A.
Stuke, K.
Danek, A.
Otto, M.
Schroeter, M. L.
JOURNAL OF NEUROCHEMISTRY, 2016, 138 : 350 - 350

← 1 2 3 4 5 →