Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage is often contradicted by legal and ethical considerations to protect the passengers’ privacy. This work proposes an end-to-end Long Short-Term Memory network with a problem-adapted cost function that learned to count boarding and alighting passengers on a publicly available, comprehensive dataset of approx.13,000 manually annotated low-resolution 3D LiDAR video recordings (depth information only) from the doorways of a regional train. These depth recordings do not allow the identification of single individuals. For each door opening phase, the trained models predict the correct passenger count (ranging from 0 to 67) in approx.96% of boarding and alighting, respectively. Repeated training with different training and validation sets confirms the independence of this result from a specific test set.

中文翻译：

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NAPC：基于隐私友好数据集的公共交通自动乘客计数神经算法

公共交通中的实时负载信息对乘客和服务提供商都非常重要。神经算法在各种对象计数任务上表现出高性能，并在开发自动乘客计数系统中发挥着不断增长的方法作用。然而，公共空间视频片段的发布往往与保护乘客隐私的法律和道德考虑相矛盾。这项工作提出了一个端到端的长短期记忆网络，该网络具有适应问题的成本函数，该网络学会在一个公开可用的、包含大约 13,000 个手动注释的低分辨率 3D LiDAR 视频记​​录的综合数据集上计算登机和下车乘客的数量（仅限深度信息）从区域火车的门口。这些深度记录不允许识别单个个体。对于每个开门阶段，经过训练的模型分别预测了大约 96% 的登机和下车的正确乘客数量（范围从 0 到 67）。使用不同的训练集和验证集进行重复训练证实了该结果与特定测试集的独立性。