@inproceedings{6dafa38d49124936aa79f520cb5b6ad4,
title = "Embedded vision based automotive interior intrusion detection system",
abstract = "Motor vehicle theft has caused massive economic loss over the world. This paper proposes an embedded vision system to detect automotive interior intrusion. The system uses a fusion of an acceleration module and a vision module to meet the requirement of low power consumption for most motor vehicles. Furthermore, an effective intrusion detection algorithm is developed for the on-board vision module. The vision system is able to detect the intrusion even in the dark night due to the employment of infrared lights. Experimental evaluation is conducted under a variety of illumination conditions, such as day time, night time and even shining light. An intrusion detection accuracy of 91:7\% is achieved, which shows that the developed embedded vision system is reliable for motor vehicle intrusion detection.",
keywords = "Intrusion detection, Motor vehicle, Vision",
author = "Haibin Cai and Donghee Lee and Hwang Joonkoo and Yinfeng Fang and Song Li and Honghai Liu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 ; Conference date: 05-10-2017 Through 08-10-2017",
year = "2017",
month = nov,
day = "27",
doi = "10.1109/SMC.2017.8123069",
language = "英语",
series = "2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2909--2914",
booktitle = "2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017",
address = "美国",
}