论文标题
为人类活动识别创建一个大规模的合成数据集
Creating a Large-scale Synthetic Dataset for Human Activity Recognition
论文作者
论文摘要
创建和标记用于培训人类活动识别模型的视频数据集是一项艰巨的任务。在本文中，我们通过使用3D渲染工具来生成视频的合成数据集，并证明对这些视频培训的分类器可以推广到真实视频。我们使用五种不同的增强技术来生成视频，从而导致各种精确标记的独特视频。我们在视频中微调了预训练的i3D模型，并发现该模型能够在三个类别的HMDB51数据集上获得73％的高精度。我们还发现，使用数据集的增加HMDB培训集可以提高分类器的性能2％。最后，我们讨论了数据集的可能扩展，包括对人的虚拟尝试和建模运动。
Creating and labelling datasets of videos for use in training Human Activity Recognition models is an arduous task. In this paper, we approach this by using 3D rendering tools to generate a synthetic dataset of videos, and show that a classifier trained on these videos can generalise to real videos. We use five different augmentation techniques to generate the videos, leading to a wide variety of accurately labelled unique videos. We fine tune a pre-trained I3D model on our videos, and find that the model is able to achieve a high accuracy of 73% on the HMDB51 dataset over three classes. We also find that augmenting the HMDB training set with our dataset provides a 2% improvement in the performance of the classifier. Finally, we discuss possible extensions to the dataset, including virtual try on and modeling motion of the people.
