Name
Abstract | ||
|---|---|---|
This investigation proposes an SVM-based state transition framework (named as STSVM) to provide better performance of discriminability for human behavior identification. The STSVM consists of several state support vector machines (SSVM) and a state transition probability (STPM). The intra-structure information and inter-structure information of a human activity are analyzed and correlated by the SSVM and STPM, respectively. The integration of the SSVM and the STPM effectively provides human behavior understanding. With a database consisting of five kinds of human behaviors: raising hand, standing up, squatting down, falling down, and sitting, the proposed algorithm has been demonstrated with a significant recognition rate of 88.6%. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICASSP.2009.4959988 | ICASSP |
Keywords | Field | DocType |
human behavior identification,image processing,inter-structure information,pattern recognition,svm-based state transition framework,dynamical human behavior identification,user interface human factors,state transition framework,better performance,support vector machine,human behavior understanding,human behavior,human activity,state transition probability,intra-structure information,support vector machines,state support vector machine,human factors,state transition,user interface | Pattern recognition,Computer science,Support vector machine,Image processing,Human behavior,Artificial intelligence,Machine learning | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4244-2354-5 | 978-1-4244-2354-5 | 2 |
PageRank | References | Authors |
0.37 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen-Yu Chen | 1 | 52 | 6.35 |
| Ta-Cheng Wang | 2 | 2 | 0.37 |
| Jhing-fa Wang | 3 | 982 | 114.31 |
| Li Pang Shieh | 4 | 2 | 0.37 |