首个面向交通场景的事件相机多目标跟踪数据集及基准
TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios
- 基于事件相机构建交通场景多目标跟踪数据集
- 提出专用特征提取器，在该数据集上实现优异追踪性能
- 适合关注智能交通与事件相机应用的研究者
在智能交通系统中，多目标跟踪主要依赖帧基摄像头。然而，这类摄像头在低光照和高速运动条件下表现不佳。事件相机具有低延迟、高动态范围和高时间分辨率的特点，有望缓解这些问题。与帧基视觉相比，事件基视觉的研究仍十分有限。为填补这一研究空白，我们引入首个针对事件基智能交通系统的试点数据集，涵盖车辆与行人检测和跟踪。基于该数据集，我们建立了一个基于检测的追踪基准，并设计了专用特征提取器，取得了优异的性能。
原文摘要 · Abstract (English)
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.
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