论文标题
校准多视图检测的两级数据增强
Two-level Data Augmentation for Calibrated Multi-view Detection
论文作者
论文摘要
数据增强已证明其在改善模型概括和性能方面的有用性。尽管它通常在计算机视觉应用中应用于多视图系统，但很少使用它。实际上，几何数据增加可以打破观点之间的一致性。这是有问题的，因为多视图数据往往很少，并且注释很昂贵。在这项工作中，我们建议通过引入新的多视图数据增强管道来解决此问题，以保持观点之间的一致性。除了传统的输入图像增强外，我们还提出了直接在现场级别应用的第二级增强。当与我们的简单多视图检测模型结合使用时，我们的两级增强管道在两个主要的多视频多人检测数据集野外野外和多维亚X上的差距大大优于所有现有基线。
Data augmentation has proven its usefulness to improve model generalization and performance. While it is commonly applied in computer vision application when it comes to multi-view systems, it is rarely used. Indeed geometric data augmentation can break the alignment among views. This is problematic since multi-view data tend to be scarce and it is expensive to annotate. In this work we propose to solve this issue by introducing a new multi-view data augmentation pipeline that preserves alignment among views. Additionally to traditional augmentation of the input image we also propose a second level of augmentation applied directly at the scene level. When combined with our simple multi-view detection model, our two-level augmentation pipeline outperforms all existing baselines by a significant margin on the two main multi-view multi-person detection datasets WILDTRACK and MultiviewX.
