RegFrame: fast recognition of simple human actions on a stand-alone mobile device

Di Han, Jianqing Li, Zihua Zeng, Xiaochen Yuan, Wenting Li

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In recent years, human action recognition in videos has become an active research topic, being applied in surveillance, security, somatic games, interactive operations, etc. Since most human action recognition systems are designed for PCs, their performance is poor when transplanted to mobile devices. In this paper, we develop a human action recognition system called “RegFrame,” which can rapidly and accurately recognize simple human actions, including 3D actions, on a stand-alone mobile device. The system divides an action recognition process into two steps: object recognition and movement detection. The movement detection is implemented by a novel Nine-Square algorithm that nearly avoids floating point computing, which improves the recognition time. The experimental results show that the proposed “RegFrame” works reliably in different testing scenarios, and it outperforms the action recognition method of the SAMSUNG Galaxy V (S5) by up to 20% in terms of action recognition time. In addition, the proposed system can be flexibly integrated with a variety of applications.

Original languageEnglish
Pages (from-to)2787-2793
Number of pages7
JournalNeural Computing and Applications
Issue number9
Publication statusPublished - 1 Nov 2018
Externally publishedYes


  • Classifier training
  • Human action recognition
  • Nine-Square algorithm


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