Currently, there is a large number of methods that use 2D poses to represent and recognize human action in videos. Most of these methods use information computed from raw 2D poses based on the straight line segments that form the body parts in a 2D pose model in order to extract features (e.g., angles and trajectories). In our work, we propose a new method of representing 2D poses. Instead of directly using the straight line segments, firstly, the 2D pose is converted to the parameter space in which each segment is mapped to a point. Then, from the parameter space, spatiotemporal features are extracted and encoded using a Bag-of-Poses approach, then used for human action recognition in the video. Experiments on two well-known public datasets, Weizmann and KTH, showed that the proposed method using 2D poses encoded in parameter space can improve the recognition rates, obtaining competitive accuracy rates compared to state-of-the-art methods.

当前，有许多方法使用2D姿势来表示和识别视频中的人类动作。这些方法中的大多数都使用基于在2D姿势模型中形成身体部位的直线段，从原始2D姿势计算出的信息，以提取特征（例如角度和轨迹）。在我们的工作中，我们提出了一种表示2D姿势的新方法。首先，不是直接使用直线线段，而是将2D姿态转换为参数空间，在该空间中每个线段都映射到一个点。然后，从参数空间中提取时空特征，并使用“姿势袋”方法进行编码，然后将其用于视频中的人为动作识别。在两个著名的公共数据集Weizmann和KTH上进行了实验，