Abstract
Abstract The subway has become the main commuting tool in most cities, and its large passenger flow has become the norm in urban rail transit. Accurate and real-time statistics of passenger flow in stations can provide scientific basis for subway management and control. Currently, it is difficult to count the one-way passenger flow in the two-way passenger flow video in the current statistical methods commonly used in the field. In this paper, two algorithms, DaSiam-RPN and Deep-Sort, which are effective in target tracking, are selected for subway passenger transportation. Real-time one-way passenger flow statistics are carried out on the two-way passenger flow in the channel scene in the station, and the accuracy, real-time and performance of the two algorithms are compared. KeywordOne-way passenger flow of subwayTarget trackingDaSiam-RPN algorithmDeep-Sort algorithm
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