MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset
This dataset addresses the need for high-quality annotated data for event-based vision research in automotive applications, but it is incremental as it builds on existing datasets by adding multi-modal features.
The authors introduced the MEVDT dataset, a multi-modal collection of synchronized event data and grayscale images for traffic scenes, comprising 63 sequences with 13k images, 5M events, 10k labels, and 85 tracking trajectories, to support object detection and tracking research in automotive environments.
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.