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Abstract
Nowadays, with the development of automotive driving technologies, more and more functions and devices with control systems based on tactile, optical, and acoustic sensors are assembled into cars. However, these systems are faced with environmental limitations such as environmental noise and illumination conditions. Moreover, operations of these systems will cause lack of concentration on driving, which is a major cause of car accidents. In order to overcome these limitations, in this paper, an infrared array sensor is applied to construct a hand posture recognition system for in-vehicle device control. In the system, 10 kinds of target hand postures and posture movements toward four directions are combined to achieve the aim of the device selection and operations. The input images are separated into images with objects and without objects. Then, images in which object appears in boundary areas as well as blurred images are removed to improve the accuracy of the system. A convolutional neural network is applied as a classifier in order to realize the recognition of the 10 target hand postures and non-target postures for the in-vehicle device selection. After that, a detection method of the posture movement directions is applied for the device operations. Both indoor and in-vehicle experiments are conducted to verify the robustness of this system, and the results show that the proposed system can overcome the disadvantages of other systems and has a wide application with high accuracy.