Skeleton motion history based human action recognition using deep learning
Cho Nilar Phyo, Thi Thi Zin, Pyke Tin · 2017
Nowadays, deep learning is very popular in a variety of research field due to its outperformance over the existing machine learning methods and its high generality over raw inputs. According to recent surveys, deep learning can give high performance in visual object recognition system. Human Action Recognition (HAR) is a promising research area over the computer vision research field due to its enormous applicability. Most of the conventional HAR need to extract the handcrafted features in advance before classifying the actions and the environments are fixed. Those limitations make HAR to depend too much on the problem. In real-world, it is difficult to choose the suitable feature depending on the problem and difficult to fix the environment. In this paper, we applied the deep learning technology over the Skeleton Motion History Image (Skl MHI) of human actions to implement HAR that can work independently on the problem domain. According to the experimental results, the proposed system achieves the high recognition accuracy with low computational cost under the various environments.