Action Recognition in Broadcast Tennis Video Using Optical Flow and Support Vector Machine

2006 ◽  
pp. 89-98 ◽  
Author(s):  
Guangyu Zhu ◽  
Changsheng Xu ◽  
Wen Gao ◽  
Qingming Huang
Keyword(s):  
Support Vector Machine ◽  
Optical Flow ◽  
Action Recognition ◽  
Support Vector

Action Recognition with Spatiotemporal Analysis and Support Vector Machine for Elderly Monitoring System

2021 ◽  
Author(s):  
Mahaputra Ilham Awal ◽  
Luqmanul Hakim Iksan ◽  
Rizky Zull Fhamy ◽  
Dwi Kurnia Basuki ◽  
Sritrusta Sukaridhoto ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Monitoring System ◽  
Action Recognition ◽  
Spatiotemporal Analysis ◽  
Support Vector

Human action recognition with group lasso regularized-support vector machine

2016 ◽  
Vol 25 (3) ◽  
pp. 033015 ◽  
Author(s):  
Huiwu Luo ◽  
Huanzhang Lu ◽  
Yabei Wu ◽  
Fei Zhao
Keyword(s):  
Support Vector Machine ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Group Lasso ◽  
Support Vector

People Counting Based on the Moving Feature of the Mass

2014 ◽  
Vol 989-994 ◽  
pp. 2540-2542
Author(s):  
Peng Zhe Qiao ◽  
Tao Li ◽  
Tao Xiang ◽  
Xi Zhi Zhang
Keyword(s):  
Support Vector Machine ◽  
Energy Density ◽  
Optical Flow ◽  
Video Surveillance ◽  
Support Vector ◽  
Svm Classifier ◽  
People Counting ◽  
The People ◽  
Flow Algorithm ◽  
Counting Model

In order to improve the accuracy of people counting in video surveillance, the method for people counting based on the moving feature of the mass is proposed. We obtain the orientation and energy density of mass through the optical flow algorithm, and get the information about the size of mass to design the feature of mass. The people counting model is obtained by training a support vector machine (SVM) classifier with the moving feature and shape feature of mass. The experimental results confirm that our approach improves the accuracy of people counting.

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