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 unicodex2014 consisting of object classifications, pixel-precise bounding boxes, and unique object IDs unicodex2014 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.