Abstract
In Intelligent Transportation Systems (ITS), multi-object tracking is primarily based on frame-based cameras. However, these cameras tend to perform poorly under dim lighting and high-speed motion conditions. Event cameras, characterized by low latency, high dynamic range and high temporal resolution, have considerable potential to mitigate these issues. Compared to frame-based vision, there are far fewer studies on event-based vision. To address this research gap, we introduce an initial pilot dataset tailored for event-based ITS, covering vehicle and pedestrian detection and tracking. We establish a tracking-by-detection benchmark with a specialized feature extractor based on this dataset, achieving excellent performance.
Abstract (translated)
在智能交通系统（ITS）中，多目标跟踪主要依赖于基于帧的摄像头。然而，在光线昏暗和高速运动条件下，这些摄像头的表现通常较差。事件相机因其低延迟、高动态范围和高时间分辨率的特点，具备解决这些问题的巨大潜力。与基于帧的视觉相比，关于基于事件的视觉的研究较少。为填补这一研究空白，我们引入了一个专门针对基于事件的ITS的初步试点数据集，该数据集涵盖了车辆和行人检测及跟踪的内容。我们基于此数据集建立了一个追踪基准测试，并采用了一种专门的功能提取器，实现了卓越的性能。
URL
https://arxiv.org/abs/2512.14595
