Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks

• Published in: IEEE transactions on circuits and systems for video technology Vol. 28; no. 3; pp. 807 - 811
• Main Authors: Hou, Yonghong, Li, Zhaoyang, Wang, Pichao, Li, Wanqing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Action recognition; Artificial neural networks; Body parts; Coding; Color texture; convolutional neural network (ConvNet); Encoding; Feature recognition; Image coding; Image color analysis; Image recognition; Joints (anatomy); Mapping; Neural networks; Skeleton; Training; Video data

This letter presents an effective method to encode the spatiotemporal information of a skeleton sequence into color texture images, referred to as skeleton optical spectra, and employs convolutional neural networks (ConvNets) to learn the discriminative features for action recognition. Such spectrum...