论文标题
实时行动识别细粒度的动作和洗手数据集
Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset
论文作者
论文摘要
在本文中，我们提出了一种用于实时操作识别的三际算法和一个新的手洗视频数据集，其目的是将动作识别与现实世界的约束结合在一起以产生有效的结论。提出了一种三潮融合算法，即使在诸如Raspberry Pi之类的低功率系统上，该算法也可以实时准确，有效地运行。所提出的算法的基石是合并空间和时间信息，以及视频中对象的信息，同时使用有效的体系结构，以及光流计算以实时获得值得称赞的结果。该算法获得的结果在UCF-101和HMDB-51数据集上进行了基准测试，分别达到92.7％和64.9％的精度。要注意的一个重要点是，该算法是新颖的，因为它也能够学习极为相似的动作之间的复杂差异，即使对于人眼，这也很困难。此外，本文还注意到数据集中的数据集数量缺乏，以识别非常相似或细粒度的动作，还引入了一个新的数据集，该数据集可公开可用，该数据集的手洗数据集则是为未来的精细颗粒动作识别任务引入新的基准标准。
In this paper we present a three-stream algorithm for real-time action recognition and a new dataset of handwash videos, with the intent of aligning action recognition with real-world constraints to yield effective conclusions. A three-stream fusion algorithm is proposed, which runs both accurately and efficiently, in real-time even on low-powered systems such as a Raspberry Pi. The cornerstone of the proposed algorithm is the incorporation of both spatial and temporal information, as well as the information of the objects in a video while using an efficient architecture, and Optical Flow computation to achieve commendable results in real-time. The results achieved by this algorithm are benchmarked on the UCF-101 as well as the HMDB-51 datasets, achieving an accuracy of 92.7% and 64.9% respectively. An important point to note is that the algorithm is novel in the aspect that it is also able to learn the intricate differences between extremely similar actions, which would be difficult even for the human eye. Additionally, noticing a dearth in the number of datasets for the recognition of very similar or fine-grained actions, this paper also introduces a new dataset that is made publicly available, the Hand Wash Dataset with the intent of introducing a new benchmark for fine-grained action recognition tasks in the future.
