Interest in Automatic People Counting (APC) for crowd detection and management is rapidly growing. While a range of Internet of Things (IoT) sensors and systems exist, video analytics is emerging as a particularly attractive option — especially for applications where more traditional methods of people counting are not available, unreliable or expensive. In this paper we focus on automatic people counting in the public transport context – specifically, rail replacement bus services – in which bus companies are typically contracted to provide bus services to replace train services during periods of planned and unplanned line disruption. This presents a particularly compelling use-case for video-based people counting, while also presenting unique challenges. Field trials are thus vital to the proper assessment of video-based APC solutions, however remain relatively scarce in the literature. While datasets to support research and benchmarking exist, these do not capture the intrinsic complexities of real-world deployment and the implications of selected configurations — in particular, on-vehicle versus off-vehicle use cases. In this paper, we evaluate our own video-based APC solution, representative of state-of-the-art approaches in the literature, in two separate extensive (i.e, multi-day) metropolitan field trials covering both on and off-bus use-cases. Through real-world deployment of the system in both settings, we highlight key differences with respect to APC accuracy, as well as other practical considerations, and the validity of underlying assumptions in both on and off-bus scenarios.

中文翻译：

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基于视频的公共交通自动人数统计：车内部署与车外部署


人们对用于人群检测和管理的自动人数统计 (APC) 的兴趣正在迅速增长。虽然存在一系列物联网 (IoT) 传感器和系统，但视频分析正在成为一种特别有吸引力的选择，尤其是对于无法使用、不可靠或昂贵的传统人数统计方法的应用而言。在本文中，我们重点关注公共交通环境中的自动人数统计，特别是铁路替代巴士服务，其中巴士公司通常签订合同，在计划内和计划外线路中断期间提供巴士服务以取代火车服务。这为基于视频的人数统计提供了一个特别引人注目的用例，同时也带来了独特的挑战。因此，现场试验对于正确评估基于视频的 APC 解决方案至关重要，但文献中仍然相对较少。虽然支持研究和基准测试的数据集存在，但这些数据集并没有捕捉到现实世界部署的内在复杂性以及所选配置的影响——特别是车载与车外用例。在本文中，我们在两个独立的广泛（即多天）都市现场试验中评估了我们自己的基于视频的 APC 解决方案，该解决方案代表了文献中最先进的方法，涵盖了公交车上和车下的使用-案例。通过在两种设置中系统的实际部署，我们强调了 APC 准确性、其他实际考虑因素以及车上和车外场景中基本假设的有效性方面的关键差异。