Abstract
Understanding human movement and city dynamics has always been challenging. From traditional methods of manually observing the city's inhabitant, to using cameras, to now using sensors and more complex technology, the field of urban monitoring has evolved greatly. Still, there are more that can be done to unlock better practices for understanding city dynamics. This paper surveys how the landscape of urban dynamics studying has evolved with a particular focus on event-based cameras. Event-based cameras capture changes in light intensity instead of the RGB values that traditional cameras do. They offer unique abilities, like the ability to work in low-light, that can make them advantageous compared to other sensors. Through an analysis of event-based cameras, their applications, their advantages and challenges, and machine learning applications, we propose event-based cameras as a medium for capturing information to study urban dynamics. They offer the ability to capture important information while maintaining privacy. We also suggest multi-sensor fusion of event-based cameras and other sensors in the study of urban dynamics. Combining event-based cameras and infrared, event-LiDAR, or vibration has to potential to enhance the ability of event-based cameras and overcome the challenges that event-based cameras have.
Abstract (translated)
理解人类运动和城市动态一直是一项挑战。从传统的手动观察城市居民，到使用相机，再到如今采用传感器和其他复杂技术，城市管理领域已经取得了巨大的进步。然而，还有更多可以做来解锁更好的实践方法以更好地了解城市动态。本文回顾了研究城市动态领域的演变，并特别关注事件驱动型摄像头（event-based cameras）。事件驱动型摄像头捕捉的是光线强度的变化而非传统相机所捕捉的RGB值。它们具有独特的功能，例如在低光条件下工作的能力，这使它们相较于其他传感器更有优势。通过对事件驱动型摄像头及其应用、优点和挑战以及机器学习应用进行分析，我们提出将事件驱动型摄像头作为研究城市动态的信息采集手段。这种技术能够捕捉重要信息的同时维护隐私。此外，我们也建议结合使用事件驱动型摄像头和其他传感器（如红外线、事件激光雷达或振动）来研究城市动态，以增强事件驱动型摄像头的能力并克服其面临的挑战。
URL
https://arxiv.org/abs/2512.11076
