Abstract
With the continuous advancement of urbanization, the urban rail transit network continues to be dense, and the number of transfer stations increases, resulting in an increase in the flow of interchange passengers. This paper introduces pedestrian detection into the statistics to realize real-time statistics of passenger transfer flow. This article first determines the research scenario and simplifies the transfer path. Then, the pedestrian detection model is constructed by using the histogram of gradient directions (HOG) and support vector machine (SVM), and combined with the continuous adaptive mean shift algorithm (CamShift) to design and realize the transfer path passenger flow statistics. Finally, the algorithm was trained and tested through the samples obtained at Xi’an Xiaozhai subway station, and the test results obtained have high accuracy.
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