论文标题
用于汽车的大型基于事件的检测数据集
A Large Scale Event-based Detection Dataset for Automotive
论文作者
论文摘要
我们介绍了第一个用于事件摄像机的非常大的检测数据集。该数据集由使用304x240 ATIS传感器获取的超过39个小时的汽车记录组成。它包含开放的道路和非常多样化的驾驶场景,包括城市,高速公路,郊区和乡村场景,以及不同的天气和照明条件。记录中包含的汽车和行人的手动边界框注释也以1到4Hz的频率提供,总共产生了超过255,000个标签。我们认为,此大小标记的数据集的可用性将有助于基于事件的视力任务(例如对象检测和分类)的重大进展。我们还期望在其他任务中的好处,例如光流,运动和跟踪的结构,例如,可以通过自我监督的学习方法利用大量数据。
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.