MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset

TL;DR

The MEVDT dataset offers synchronized event and image data for vehicle detection and tracking, supporting advancements in event-based vision for automotive applications.

Contribution

This paper introduces the first comprehensive multi-modal dataset with synchronized event and image data, detailed annotations, and trajectories for vehicle detection and tracking.

Findings

Provides 13k images and 5 million events for research

Includes 10,000 object labels and 85 tracking trajectories

Enables evaluation of event-based detection and tracking algorithms

Abstract

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.