Embedded vision based automotive interior intrusion detection system
Haibin Cai, Donghee Lee, Hwang Joonkoo, Yinfeng Fang, Song Li, Honghai Liu · 2017
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.