论文标题
对象计数的域随机化
Domain Randomization for Object Counting
论文作者
论文摘要
最近，已经证明了基于游戏引擎的合成数据集的使用可以改善计算机视觉中多个任务的性能。但是，这些数据集通常仅适用于计算机游戏中描绘的特定域，例如涉及车辆和人员的城市场景。在本文中，我们提出了一种生成合成数据集的方法，用于对象计数任何域，而无需由昂贵的3D艺术家团队手动生成的照片真实技术。我们引入了一种基于合成数据集的域随机化方法，用于对象计数，这些数据集快速且便宜地生成。我们故意避免了光真相，并大大提高数据集的变异性，从而产生具有随机纹理和3D变换的图像，从而改善了概括。实验表明，我们的方法促进了多个域的各种真实单词对象计数数据集的良好性能：人，车辆，企鹅和水果。源代码可在以下网址获得：https：//github.com/enric1994/dr4oc
Recently, the use of synthetic datasets based on game engines has been shown to improve the performance of several tasks in computer vision. However, these datasets are typically only appropriate for the specific domains depicted in computer games, such as urban scenes involving vehicles and people. In this paper, we present an approach to generate synthetic datasets for object counting for any domain without the need for photo-realistic techniques manually generated by expensive teams of 3D artists. We introduce a domain randomization approach for object counting based on synthetic datasets that are quick and inexpensive to generate. We deliberately avoid photorealism and drastically increase the variability of the dataset, producing images with random textures and 3D transformations, which improves generalization. Experiments show that our method facilitates good performance on various real word object counting datasets for multiple domains: people, vehicles, penguins, and fruit. The source code is available at: https://github.com/enric1994/dr4oc
