Embedded vision based automotive interior intrusion detection system

Haibin Cai, Donghee Lee, Hwang JoonKoo, Yinfeng Fang, Song Li, Honghai Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
PublisherIEEE
ISBN (Electronic)978-1538616451
ISBN (Print)978-1538616468
DOIs
Publication statusPublished - 1 Dec 2017
Event2017 IEEE Conference on Systems, Man, and Cybernetics - Banff, Alberta, Canada
Duration: 5 Oct 20178 Oct 2017

Conference

Conference2017 IEEE Conference on Systems, Man, and Cybernetics
Abbreviated titleSMC 2017
Country/TerritoryCanada
CityBanff, Alberta
Period5/10/178/10/17

Keywords

  • intrusion detection
  • vision
  • motor vehicle

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