用随机多边形和曲线运动增强事件数据，提升自动驾驶视觉精度。
ShapeAug++: More Realistic Shape Augmentation for Event Data
- 用随机生成多边形与曲线轨迹模拟真实物体运动
- 在多个事件数据集上提升最高3.7%的分类准确率
- 适合做事件相机数据增强的研究者与开发者
新型动态视觉传感器（DVS）因低延迟、高动态范围和低功耗，受到广泛关注，尤其适用于自动驾驶场景，能有效缓解运动模糊并实现夜视。然而，在真实自动驾驶环境中，遮挡问题仍存在——前景物体遮挡背景交通参与者。现有方法ShapeAug通过模拟直线路径移动物体产生的事件进行数据增强，但其形状与运动模式过于简单，难以反映真实世界复杂行为。本文提出ShapeAug++，在ShapeAug基础上引入随机生成的多边形及曲线运动轨迹，更真实地模拟物体运动。实验表明，该方法在多个DVS分类数据集上表现更优，相较ShapeAug，Top-1准确率最高提升3.7%。
原文摘要 · Abstract (English)
The novel Dynamic Vision Sensors (DVSs) gained a great amount of attention recently as they are superior compared to RGB cameras in terms of latency, dynamic range and energy consumption. This is particularly of interest for autonomous applications since event cameras are able to alleviate motion blur and allow for night vision. One challenge in real-world autonomous settings is occlusion where foreground objects hinder the view on traffic participants in the background. The ShapeAug method addresses this problem by using simulated events resulting from objects moving on linear paths for event data augmentation. However, the shapes and movements lack complexity, making the simulation fail to resemble the behavior of objects in the real world. Therefore in this paper, we propose ShapeAug++, an extended version of ShapeAug which involves randomly generated polygons as well as curved movements. We show the superiority of our method on multiple DVS classification datasets, improving the top-1 accuracy by up to 3.7% compared to ShapeAug.
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