TUMTraf EMOT: 事件基多目标跟踪数据集及交通场景的基线
TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios
AI总结：
TUMTraf EMOT提出事件基多目标跟踪数据集及基线，用于交通场景中的车辆和行人检测与跟踪，通过专用特征提取器实现高效跟踪。
摘要：
在智能交通系统（ITS）中，多目标跟踪主要基于帧级相机。然而，这些相机在低光照和高速运动条件下表现较差。事件相机以低延迟、高动态范围和高时间分辨率著称，有潜力缓解这些问题。与帧级视觉相比，事件基视觉的研究远少。为填补这一研究空白，我们引入了一个初步的事件基ITS数据集，涵盖车辆和行人检测与跟踪。我们基于此数据集建立了一个基于检测的跟踪基准，使用专门的特征提取器，取得了出色的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
