A Large Scale Event-based Detection Dataset for Automotive
This dataset addresses the need for labeled data in event-based vision, potentially advancing tasks like object detection and classification for automotive applications, though it is incremental as it focuses on data collection rather than new methods.
The authors introduced the first large-scale detection dataset for event cameras, containing over 39 hours of automotive recordings with diverse driving scenarios and conditions, resulting in more than 255,000 manual bounding box annotations for cars and pedestrians.
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.