Recently, Dynamic Vision Sensors (DVSs) sparked a lot of interest due to their inherent advantages over conventional RGB cameras. These advantages include a low latency, a high dynamic range and a low energy consumption. Nevertheless, the processing of DVS data using Deep Learning (DL) methods remains a challenge, particularly since the availability of event training data is still limited. This leads to a need for event data augmentation techniques in order to improve accuracy as well as to avoid over-fitting on the training data. Another challenge especially in real world automotive applications is occlusion, meaning one object is hindering the view onto the object behind it. In this paper, we present a novel event data augmentation approach, which addresses this problem by introducing synthetic events for randomly moving objects in a scene. We test our method on multiple DVS classification datasets, resulting in an relative improvement of up to 6.5 % in top1-accuracy. Moreover, we apply our augmentation technique on the real world Gen1 Automotive Event Dataset for object detection, where we especially improve the detection of pedestrians by up to 5 %.

翻译：近期，动态视觉传感器因其相较于传统RGB相机的固有优势而引发广泛关注，包括低延迟、高动态范围和低能耗。然而，利用深度学习方法处理动态视觉传感器数据仍面临挑战，尤其是事件训练数据的可用性仍然有限。这促使我们需要开发事件数据增强技术以提高准确性，并避免在训练数据上过拟合。另一个现实世界中（尤其是汽车应用场景）的挑战是遮挡问题——即一个物体遮挡了其后方的物体。本文提出一种新颖的事件数据增强方法，通过为场景中随机移动的物体引入合成事件来解决该问题。我们在多个DVS分类数据集上测试该方法，在top1-准确率上实现了最高6.5%的相对提升。此外，我们将该增强技术应用于真实世界的Gen1汽车事件数据集进行目标检测，特别将行人的检测性能提升了最高5%。