用事件相机监测城市人流，兼顾隐私与低光表现。
E-CHUM: Event-based Cameras for Human Detection and Urban Monitoring
- 用事件相机捕捉光强变化，替代传统RGB图像。
- 在低光环境下仍能有效感知行人，且保护隐私。
- 适合城市监控、智能交通等需隐私保护的场景。
理解人类活动与城市动态一直颇具挑战。从早期人工观察，到使用传统摄像头，再到如今借助传感器与复杂技术，城市监测手段已显著演进。然而，仍有提升空间以更深入理解城市运行机制。本文聚焦事件相机在城市动态研究中的应用，系统回顾其发展脉络。事件相机不记录静态图像，而是实时感知光照变化，具备低功耗、高动态范围和低延迟优势，尤其适用于低光环境。通过分析其在行人检测、行为识别中的应用潜力，以及与红外、事件-LiDAR、振动传感器融合的可能性，本文提出将事件相机作为城市监测的核心信息采集工具。多源传感器融合可弥补事件相机在静态场景感知弱、数据稀疏等问题，显著提升整体监测能力。该方案在保障隐私的前提下，实现高效、鲁棒的城市动态感知。
原文摘要 · Abstract (English)
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.
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