Support Vector Machine for Behavior-Based Driver Identification System
We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2010-01-01
|
| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2010/397865 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566580664336384 |
|---|---|
| author | Huihuan Qian Yongsheng Ou Xinyu Wu Xiaoning Meng Yangsheng Xu |
| author_facet | Huihuan Qian Yongsheng Ou Xinyu Wu Xiaoning Meng Yangsheng Xu |
| author_sort | Huihuan Qian |
| collection | DOAJ |
| description | We present an intelligent driver
identification system to handle vehicle theft based on modeling
dynamic human behaviors. We propose to recognize illegitimate
drivers through their driving behaviors. Since human driving
behaviors belong to a dynamic biometrical feature which is
complex and difficult to imitate compared with static features
such as passwords and fingerprints, we find that this novel
idea of utilizing human dynamic features for enhanced security
application is more effective. In this paper, we first describe
our experimental platform for collecting and modeling human
driving behaviors. Then we compare fast Fourier transform
(FFT), principal component analysis (PCA), and independent
component analysis (ICA) for data preprocessing. Using machine
learning method of support vector machine (SVM), we derive the individual
driving behavior model and we then demonstrate
the procedure for recognizing different drivers by analyzing
the corresponding models. The experimental results of learning
algorithms and evaluation are described. |
| format | Article |
| id | doaj-art-e54d3e2035584589a2857fa8b9d80ba2 |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-e54d3e2035584589a2857fa8b9d80ba22025-02-03T01:03:46ZengWileyJournal of Robotics1687-96001687-96192010-01-01201010.1155/2010/397865397865Support Vector Machine for Behavior-Based Driver Identification SystemHuihuan Qian0Yongsheng Ou1Xinyu Wu2Xiaoning Meng3Yangsheng Xu4Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong KongShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaWe present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.http://dx.doi.org/10.1155/2010/397865 |
| spellingShingle | Huihuan Qian Yongsheng Ou Xinyu Wu Xiaoning Meng Yangsheng Xu Support Vector Machine for Behavior-Based Driver Identification System Journal of Robotics |
| title | Support Vector Machine for Behavior-Based Driver Identification System |
| title_full | Support Vector Machine for Behavior-Based Driver Identification System |
| title_fullStr | Support Vector Machine for Behavior-Based Driver Identification System |
| title_full_unstemmed | Support Vector Machine for Behavior-Based Driver Identification System |
| title_short | Support Vector Machine for Behavior-Based Driver Identification System |
| title_sort | support vector machine for behavior based driver identification system |
| url | http://dx.doi.org/10.1155/2010/397865 |
| work_keys_str_mv | AT huihuanqian supportvectormachineforbehaviorbaseddriveridentificationsystem AT yongshengou supportvectormachineforbehaviorbaseddriveridentificationsystem AT xinyuwu supportvectormachineforbehaviorbaseddriveridentificationsystem AT xiaoningmeng supportvectormachineforbehaviorbaseddriveridentificationsystem AT yangshengxu supportvectormachineforbehaviorbaseddriveridentificationsystem |