MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset
<|reference_start|>MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset: 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 $\unicode{x2014}$ consisting of object classifications, pixel-precise bounding boxes, and unique object IDs $\unicode{x2014}$ 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.<|reference_end|>
@article{shair2024mevdt:,
    title={MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset},
    author={Zaid A. El Shair and Samir A. Rawashdeh},
    journal={arXiv preprint arXiv:2407.20446},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20446},
    primaryClass={cs.CV cs.DB}
}