A Large Scale Event-based Detection Dataset for Automotive

TL;DR

This paper presents a large-scale, diverse dataset of automotive event camera recordings with extensive annotations, aiming to advance event-based vision tasks like object detection, classification, and tracking.

Contribution

It introduces the first large, annotated dataset for automotive event cameras, covering diverse scenarios and conditions to facilitate research and development.

Findings

Contains over 39 hours of data with 255,000 labels

Enables progress in object detection and classification for event cameras

Supports self-supervised learning and other vision tasks

Abstract

We introduce the first very large detection dataset for event cameras. The dataset is composed of more than 39 hours of automotive recordings acquired with a 304x240 ATIS sensor. It contains open roads and very diverse driving scenarios, ranging from urban, highway, suburbs and countryside scenes, as well as different weather and illumination conditions. Manual bounding box annotations of cars and pedestrians contained in the recordings are also provided at a frequency between 1 and 4Hz, yielding more than 255,000 labels in total. We believe that the availability of a labeled dataset of this size will contribute to major advances in event-based vision tasks such as object detection and classification. We also expect benefits in other tasks such as optical flow, structure from motion and tracking, where for example, the large amount of data can be leveraged by self-supervised learning methods.