Abstract: To address the problem of low intelligence and flexibility of existing flow limiting facilities, a new optimization method for flow-limiting facilities in rail transit stations based on optical feature descriptors, is proposed. First, the region of interest (ROI) is set according to the scene characteristics of rail transit stations to reduce the computation of subsequent operation. Then, the features of image sequence are analyzed by establishing optical feature descriptors. Finally, the one-class SVM is adjusted according to the clumped features of pedestrians to make condition detection possible. Experimental results demonstrate that the proposed method can detect the overload status accurately, improve the automatic level of flow-limiting facilities effectively, and provides data support and theoretical basis for organization and management of pedestrians in rail transit stations.

Key words: intelligent transportation, condition detection, feature descriptor, subway flow-limiting, one-class SVM