Towards human behavior recognition based on spatio temporal features and support vector machines

2017 ◽  
Author(s):  
Sawsen Ghabri ◽  
Wael Ouarda ◽  
Adel M. Alimi
Keyword(s):  
Support Vector Machines ◽  
Human Behavior ◽  
Support Vector ◽  
Behavior Recognition ◽  
Temporal Features ◽  
Vector Machines ◽  
Spatio Temporal ◽  
Human Behavior Recognition

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

scholarly journals Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

2021 ◽  
Vol 10 (12) ◽  
pp. e452101220804
Author(s):  
Cecilia Cordeiro da Silva ◽  
Clarisse Lins de Lima ◽  
Ana Clara Gomes da Silva ◽  
Giselle Machado Magalhães Moreno ◽  
Anwar Musah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Aedes Aegypti ◽  
Temporal Analysis ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Monitoring And Control ◽  
Linear Regression Models ◽  
Vector Machines ◽  
Spatio Temporal

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation.

scholarly journals Human Behavior Recognition Method based on Two-layer LSTM Network with Attention Mechanism

2021 ◽  
Vol 2093 (1) ◽  
pp. 012006
Author(s):  
Zhijun Gao ◽  
Qiaoyu Gu ◽  
Zhonghua Han
Keyword(s):  
Human Behavior ◽  
Short Term Memory ◽  
Recognition Rate ◽  
Attention Mechanism ◽  
Utilization Efficiency ◽  
Experimental Result ◽  
Support Vector ◽  
Behavior Recognition ◽  
Lstm Network ◽  
Human Behavior Recognition

Abstract Aiming at the problem that the exiting human skeleton-based action recognition methods cannot fully extract the relevant information before and after the action, resulting in low utilization efficiency of skeleton points, we propose a two-layer LSTM (long short term memory) network with attention mechanism. The network has two layers, the first LSTM network is used for skeleton coding and initialization of system storage units and the second LSTM network integrates attention mechanism to further process the data of the first layer network. An algorithm is designed to assign different weights to skeleton points according to the importance of human body, which greatly increases the recognition accuracy. Action classification is accomplished by multiple support vector machines. Through training and testing, the average recognition rate of 98.5% is achieved on KTH dataset. The experimental result shows that the proposed method is effective in human behavior recognition.

Spatio-Temporal Processing for Multichannel Biosensors Using Support Vector Machines

2006 ◽  
Vol 6 (6) ◽  
pp. 1644-1651 ◽  
Author(s):  
Yueming Zuo ◽  
Shantanu Chakrabartty ◽  
Zarini Muhammad-Tahir ◽  
Sudeshna Pal ◽  
Evangelyn C. Alocilja
Keyword(s):  
Support Vector Machines ◽  
Temporal Processing ◽  
Support Vector ◽  
Vector Machines ◽  
Spatio Temporal
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