Research on the statistical analysis method of urban transit passengers based on millimeter wave radar
Real-time statistics on the number of passengers getting on and off the train is a key link in the realization of smart rail transit.However,most contemporary optical-based video statistics methods have certain defects in complex and high-density crowd flow scenes.In this regard,this article proposed a solution for counting passengers on and off trains based on millimeter wave radar.Firstly,the distance,azimuth and velocity of the refractive point of passengers were extracted by FFT(Fast Fourier Transformation)and CFAR(Constant False-Alarm Rate).Secondly,DBSCAN(Density-Based Spatial Clustering of Applications with Noise)was introduced to solve the occlusion issue under the condition of dense people flow,which is able to effectively recognize and differentiate different groups of passengers in crowded environments.Thirdly,to realize the continuous dynamic tracking of passenger targets,an intelligent tracking technique based on Kalman filtering is proposed,which could ensure the stable tracking of the target passengers even in the case of rapid changes in people flow.The effectiveness of the scheme is demonstrated through a series of arithmetic validations.Finally,in the station simulation test,the scheme still demonstrates high tracking accuracy under dense crowd conditions.
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