Real-time gesture control for automotive infotainment system

Abstract

Drivers’ distraction is one of the leading causes for road accidents. To reduce driver distraction, the real-time gesture recognition system for ADAS is aimed to simplify and enhance the interaction between human and computer by implementing the vision-based technique that allows driver to interact with the vehicle infotainment system functions using natural mid-air hand gestures. Therefore, in this paper, we proposed to track and recognize human static hand gestures. The system process is separated into five steps which include the image acquisition, the background subtraction, the hand segmentation, the features extraction, and the gesture recognition. Firstly, the image frame will be captured and resized. Thereafter, the region of interest is determined to minimize the required processing to increase performance. Next, the foreground model is being extracted and being converted to HSV color space. Then, the skin filter is applied to extract skin region. In the next step, the image is being transformed into binary image by thresholding and smoothened by applying the morphological transformation. Contour detection and approximation were being implemented. Finally, the hand features will be extracted to build the gesture recognition model such as hand center, palm radius, fingertips, defect points, hull area, hand area and angle of finger. Experimental results show that the system is able to achieve 86.25% recognition accuracy in room environment and 80% recognition accuracy in car environment. In comparison, the average classification accuracy is 92.5% and 90%, for room and car environment, respectively.