An Estimation Method of Visibility Level on Winter Road Based on Multiple Features in CCTV Images

Since drivers acquire a lot of information with eyes, the estimation of the level of visibility (visibility level) on the winter roads contributes toward enhancing traffic safety. Therefore, this paper proposes a method for estimating the visibility level on the winter roads from closed-circuit television (CCTV) images. In the proposed method, the visibility level of each image is estimated based on features acquired via Fourier transform and Convolutional Neural Network (CNN). Specifically, by constructing support vector machines (SVMs) for each feature, the probabilities of the visibility level are calculated. Furthermore, based on a comparison of the calculated probabilities of SVMs, the proposed method estimates the visibility level accurately. Experimental results show the effectiveness of the proposed method.