Wearable Vision Assistance System Based on Binocular Sensors for Visually Impaired Users

Blind or visually impaired people face special difficulties in daily life. With the advances in vision sensors and computer vision, the design of wearable vision assistance system is promising. In order to improve the life quality of the visually impaired group, a wearable system is proposed in this paper. Typically the performance of visual sensors is affected by a variety of complex factors in practice, resulting in a large number of noise and distortion. In this paper, we will creatively leverage image quality evaluation to select the captured images through vision sensors, which can ensure the input quality of scenes for the final identification system. First, we use binocular vision sensors to capture images in a fixed frequency and choose the informative ones based on stereo image quality assessment. Then the captured images will be sent to cloud for further computing. Specially, the detection and automatic result will be done for all the received images. Convolutional neural network based on big data will be used in this step. According to image analysis, the cloud computing can return the requested information for users, which can help them make a more reasonable decision in further action. Simulations and experiments show that the proposed method can solve the problem effectively. In addition, statistical results also demonstrate that wearable vision system can make visually impaired group more satisfied in visual needed situations.