El Shair Zaid A, Rawashdeh Samir A
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, 48128 MI, USA.
Data Brief. 2024 Dec 9;58:111205. doi: 10.1016/j.dib.2024.111205. eCollection 2025 Feb.
In this data article, we introduce the Multi-Modal Event-based Vehicle Detection and Tracking (MEVDT) dataset. This dataset provides a synchronized stream of event data and grayscale images of traffic scenes, captured using the Dynamic and Active-Pixel Vision Sensor (DAVIS) 240c hybrid event-based camera. MEVDT comprises 63 multi-modal sequences with approximately 13k images, 5M events, 10k object labels, and 85 unique object tracking trajectories. Additionally, MEVDT includes manually annotated ground truth labels - consisting of object classifications, pixel-precise bounding boxes, and unique object IDs - which are provided at a labeling frequency of 24 Hz. Designed to advance the research in the domain of event-based vision, MEVDT aims to address the critical need for high-quality, real-world annotated datasets that enable the development and evaluation of object detection and tracking algorithms in automotive environments.
在本数据文章中，我们介绍了多模态基于事件的车辆检测与跟踪（MEVDT）数据集。该数据集提供了交通场景的事件数据和灰度图像同步流，这些数据和图像是使用动态有源像素视觉传感器（DAVIS）240c混合基于事件的相机捕获的。MEVDT包含63个多模态序列，约有13k张图像、500万个事件、10k个对象标签以及85条独特的对象跟踪轨迹。此外，MEVDT还包括手动标注的地面真值标签——由对象分类、像素精确的边界框和独特的对象ID组成——这些标签以24Hz的标注频率提供。MEVDT旨在推动基于事件的视觉领域的研究，旨在满足对高质量、真实世界标注数据集的迫切需求，这些数据集能够促进汽车环境中对象检测和跟踪算法的开发与评估。
