NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles

Annotated imagery capturing pedestrians and vehicles in an urban environment can be used to train Neural Networks (NNs) for machine vision tasks. This paper presents the first open-source aerial neuromorphic dataset that captures pedestrians and vehicles moving in an urban environment. The dataset, titled NU-AIR, features 70.75 minutes of event footage acquired with a 640 x 480 resolution neuromorphic sensor mounted on a quadrotor operating in an urban environment. Crowds of pedestrians, different types of vehicles, and street scenes at a busy urban intersection are captured at different elevations and illumination conditions. Manual bounding box annotations of vehicles and pedestrians contained in the recordings are provided at a frequency of 30 Hz, yielding 93,204 labels in total. Evaluation of the dataset's fidelity is performed by training three Spiking Neural Networks (SNNs) and ten Deep Neural Networks (DNNs). The mean average precision (mAP) accuracy results achieved for the testing set evaluations are on-par with results reported for similar SNNs and DNNs on established neuromorphic benchmark datasets. All data and Python code to voxelize the data and subsequently train SNNs/DNNs has been open-sourced.