Embedded vision based automotive interior intrusion detection system

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

doi:10.1109/SMC.2017.8123069

Embedded vision based automotive interior intrusion detection system

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

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	978-1538616451
ISBN (Print)	978-1538616468
DOIs	https://doi.org/10.1109/SMC.2017.8123069
Publication status	Published - 1 Dec 2017
Event	2017 IEEE Conference on Systems, Man, and Cybernetics - Banff, Alberta, Canada Duration: 5 Oct 2017 → 8 Oct 2017

Conference

Conference	2017 IEEE Conference on Systems, Man, and Cybernetics
Abbreviated title	SMC 2017
Country/Territory	Canada
City	Banff, Alberta
Period	5/10/17 → 8/10/17

Keywords

intrusion detection
vision
motor vehicle

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10.1109/SMC.2017.8123069

Exploring the Relation between EMG Sampling Frequency and Hand Motion Recognition Accuracy
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Accepted author manuscript (Post-print), 3.52 MB

Cite this

@inproceedings{1ff164f9e82748e697bc3cc477b31b5a,

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, vision, motor vehicle",

author = "Haibin Cai and Donghee Lee and Hwang JoonKoo and Yinfeng Fang and Song Li and Honghai Liu",

year = "2017",

month = dec,

day = "1",

doi = "10.1109/SMC.2017.8123069",

language = "English",

isbn = "978-1538616468 ",

booktitle = "2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2017 IEEE Conference on Systems, Man, and Cybernetics, SMC 2017 ; Conference date: 05-10-2017 Through 08-10-2017",

}

Cai, H, Lee, D, JoonKoo, H, Fang, Y, Li, S & Liu, H 2017, Embedded vision based automotive interior intrusion detection system. in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Conference on Systems, Man, and Cybernetics, Banff, Alberta, Canada, 5/10/17. https://doi.org/10.1109/SMC.2017.8123069

TY - GEN

T1 - Embedded vision based automotive interior intrusion detection system

AU - Cai, Haibin

AU - Lee, Donghee

AU - JoonKoo, Hwang

AU - Fang, Yinfeng

AU - Li, Song

AU - Liu, Honghai

PY - 2017/12/1

Y1 - 2017/12/1

N2 - 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.

AB - 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.

KW - intrusion detection

KW - vision

KW - motor vehicle

U2 - 10.1109/SMC.2017.8123069

DO - 10.1109/SMC.2017.8123069

M3 - Conference contribution

SN - 978-1538616468

BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Conference on Systems, Man, and Cybernetics

Y2 - 5 October 2017 through 8 October 2017

ER -

Embedded vision based automotive interior intrusion detection system

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Keywords

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