Skeleton Based Temporal Action Detection with YOLO
Jun Wu, Yu Li, Liuqing Wang, Ke Wang, Ruifeng Li, Tianxiang Zhou · Journal of Physics Conference Series · 2019
Detecting actions in untrimmed sequences is an important yet challenging task. In this paper, we innovatively transform the temporal action detection issue into the object detection issue. Our method allows for real-time detection and end-to-end training. It consists of two stages. Firstly, we propose an idea to represent action sequences as images as well as preserving the original temporal dynamics and spatial structure information. Secondly, based on such description, we design a one-dimensional YOLO network to detect human action. In addition, we make a dataset for skeleton based temporal action detection. Experiments on our dataset demonstrate the superiority of our method.