Feature pyramid graph convolutional networks for temporal action detection

In some long and untrimmed videos, locating the important and key segments can be a very challenging task for temporal action detection. Current methods make remarkable progress when it comes to RGB images. The aim of this paper is to develop a method with the assistance of dynamic model of human body skeletons to address this problem. To this end, we propose a Feature Pyramid Graph Convolutional Network (FP-GCN). The introduced model contains a Feature Encoding Module to encode skeleton data with graph convolutional networks, a Feature Pyramid Module to exploit the inherent pyramidal hierarchy and an Action Detection Module to generate the final prediction results of the detection. We experiment our approach on NTU RGB+D and THUMOS14 datasets and obtain a satisfactory result.