Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition

no code implementations • The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019 2019 • Duohan Liang, Guoliang Fan, Guangfeng Lin, Wanjun Chen, Xiaorong Pan, Hong Zhu

In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments.

Ensemble Learning Skeleton Based Action Recognition