中南大学学报（英文版）
中南大學學報（英文版）
중남대학학보（영문판）
Journal of Central South University
2015年
10期
3916-3924
,共9页
traffic dangers%visual attention model%sparse sampling%Bayesian probability model%multiscale saliency
A method to detect traffic dangers based on visual attention model of sparse sampling was proposed. The hemispherical sparse sampling model was used to decrease the amount of calculation which increases the detection speed. Bayesian probability model and Gaussian kernel function were applied to calculate the saliency of traffic videos. The method of multiscale saliency was used and the final saliency was the average of all scales, which increased the detection rates extraordinarily. The detection results of several typical traffic dangers show that the proposed method has higher detection rates and speed, which meets the requirement of real-time detection of traffic dangers.
