TY - GEN
T1 - Hand Gesture Recognition System for In-car Device Control Based on Infrared Array Sensor
AU - Tateno, Shigeyuki
AU - Zhu, Yiwei
AU - Meng, Fanxing
PY - 2019/9
Y1 - 2019/9
N2 - Nowadays, with the development of automotive industry, more and more functions and devices are assembled in cars to improve driving experience. Meanwhile, traffic accidents are increasing in recent years. Operations of in-car device human machine interface (HMI) will cause lack of concentration, which is a major cause of the accidents. Common in-car device HMI systems are based on optics, acoustics, and so on, which are faced with environment limitations such as influenced by illumination conditions. In order to deal with these limitations, in this paper, an infrared array sensor is applied to construct a hand gesture recognition system for in-car devices control. The proposed system can overcome disadvantages of other systems and has a wider application. In the system, seven different shapes of hand and movement toward four directions are combined to achieve the aim of device operations. In data processing, convolutional neural network (CNN) is applied to realize recognition. Simulated experiments are conducted to verify the feasibility of this system.
AB - Nowadays, with the development of automotive industry, more and more functions and devices are assembled in cars to improve driving experience. Meanwhile, traffic accidents are increasing in recent years. Operations of in-car device human machine interface (HMI) will cause lack of concentration, which is a major cause of the accidents. Common in-car device HMI systems are based on optics, acoustics, and so on, which are faced with environment limitations such as influenced by illumination conditions. In order to deal with these limitations, in this paper, an infrared array sensor is applied to construct a hand gesture recognition system for in-car devices control. The proposed system can overcome disadvantages of other systems and has a wider application. In the system, seven different shapes of hand and movement toward four directions are combined to achieve the aim of device operations. In data processing, convolutional neural network (CNN) is applied to realize recognition. Simulated experiments are conducted to verify the feasibility of this system.
KW - Hand Gesture Recognition
KW - Infrared Array Sensor
UR - http://www.scopus.com/inward/record.url?scp=85073874818&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073874818&partnerID=8YFLogxK
U2 - 10.23919/SICE.2019.8859832
DO - 10.23919/SICE.2019.8859832
M3 - Conference contribution
AN - SCOPUS:85073874818
T3 - 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019
SP - 701
EP - 706
BT - 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019
Y2 - 10 September 2019 through 13 September 2019
ER -
