Real-time visibility distance evaluation based on monocular and dark channel prior

A real time traffic meteorological visibility distance evaluation algorithm in foggy weather by using dark channel prior and lane detection methodology is proposed in this paper. In foggy image, dark channel prior directly provides accurate transmission estimation. A novel lane detection algorithm which is called variable box search VBS is proposed in this paper. This novel algorithm only needs little running time and could maintain real time procession. Background generating and updating method which is called Gaussian mixture model GMM will be used to get clear background image. Two endpoints of one traffic lane are marked and saved; these data will be served for traffic scene distance calculation. Extinction coefficient k could be calculated by these two end points transmission division based on the monocular model and dark channel prior. Finally, the meteorological visibility will be according to definition from International Commission on Illumination. According to the traditional fog sorting methodology, we fulfil fog category method by our algorithm based on the extinction coefficient value. Experimental data are taken from the actual traffic scene and network data. Experimental results verify the effectiveness of this proposed algorithm.