基于SORT算法和光流模型的地铁车站
蒲一超
Detection Method of Metro Station Passenger Flow Movement Trajectory Based on SORT Algorithm and Optical Flow Model
PU Yichao
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作者信息:1.上海申通地铁集团有限公司， 201103， 上海
2.同济大学电子与信息工程学院， 201804， 上海
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Affiliation:Shanghai Shentong Metro Co., Ltd., 201103, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.07.002
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中图分类号/CLCN:U293.13
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栏目/Col:学术专论
摘要:
目的：精准监测地铁系统的大客流状态是进行客流风险识别和管控的基础。地铁车站容纳空间有限，易发生客流堆积和冲突等风险事件，亟需研究一套能够对地铁车站的客流运动轨迹进行实时识别的方法。方法：提出了基于SORT（简单的在线实时多目标跟踪）算法和光流模型的地铁车站客流运动轨迹识别方法，作为地铁安全防控的辅助手段。首先，采用SORT算法，实现高密度场景下的实时客流识别；其次，使用PWC-Net（使用金字塔、仿射变换和成本体积的光流卷积神经网络）光流模型，实现实时客流定位和运动轨迹识别；最后，通过上海市地铁车站的真实视频数据，对该方法进行验证。结果及结论：通过上述识别方法，可有效地关联目标，识别覆盖的客流，并提升识别的实时性。采用PWC-Net光流模型实现客流运动轨迹的识别，在模型的体积和训练时间上分别为FlowNet2（数据集训练顺序的网络）模型的94.12%和50.00%。因此，该方法能够满足地铁车站实时高密度客流运动轨迹识别的场景要求。
Abstracts:
Objective: Accurately monitoring the status of large passenger flow in metro system is the basis for recognizing and managing passenger flow risks. Metro stations have limited spatial capacity and are prone to risky incidents such as passenger flow congestion and conflicts, making it necessary to develop a method for real-time recognition of PMT (passenger flow movement trajectories) in metro stations. Method: A method is proposed for recognizing metro station PMT based on SORT (simple on-line and real-time tracking) algorithm and optical flow model, which serves as an auxiliary means for metro safety protection and control. Firstly, the SORT algorithm is adopted to achieve real-time passenger flow recognition in high-density scenarios; secondly, the PWC-Net (pyramidal, warping and cost volume optical convolutional neural network) optical flow model is employed for real-time passenger flow locating and PMT recognition; finally, the proposed method is validated using real station video data of Shanghai Metro. Result & Conclusion: The above-mentioned recognition method effectively associates targets, recognizes overlapping passenger flows, and improves the real-time nature of recognition. By employing the PWC-Net optical flow model for recognizing PMT, the model size and training time are 94.12% and 50.00% of the FlowNet2 (network based on dataset training sequence) model respectively. Therefore, this method can meet the scenario requirements of real-time recognition of high-density PMT in metro stations.
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