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Vision-based methods for visibility estimation can play a critical role inreducing traffic accidents caused by fog and haze. To overcome thedisadvantages of current visibility estimation methods, we present a noveldata-driven approach based on Gaussian image entropy and piecewise stationarytime series analysis (SPEV). This is the first time that Gaussian image entropyis used for estimating atmospheric visibility. To lessen the impact oflandscape and sunshine illuminance on visibility estimation, we used region ofinterest (ROI) analysis and took into account relative ratios of image entropy,to improve estimation accuracy. We assume fog and haze cause blurred images andthat fog and haze can be considered as a piecewise stationary signal. We usedpiecewise stationary time series analysis to construct the piecewise causalrelationship between image entropy and visibility. To obtain a real-worldvisibility measure during fog and haze, a subjective assessment was establishedthrough a study with 36 subjects who performed visibility observations.Finally, a total of two million videos were used for training the SPEV modeland validate its effectiveness. The videos were collected from the constantlyfoggy and hazy Tongqi expressway in Jiangsu, China. The contrast model ofvisibility estimation was used for algorithm performance comparison, and thevalidation results of the SPEV model were encouraging as 99.14% of the relativeerrors were less than 10%.

Expressway visibility estimation based on image entropy and piecewise stationary time series analysis