Human 3D motion recognition based on spatial-temporal context of joints

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

2 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Pages2740-2743
Publication statusPublished - 2010

Publication series

Name
ISSN (Print)1051-4651

Conference

Title2010 20th International Conference on Pattern Recognition, ICPR 2010
PlaceTurkey
CityIstanbul
Period23 - 26 August 2010

Abstract

The paper presents a novel human motion recognition method based on a new form of the Hidden Markov Models, called spatial-temporal hidden markov models (ST-HMM), which can be learnt from a sequence of joints positions. To cope with the high dimensionality of the pose space, in this paper, we exploit the spatial dependency between each pair of spatially connected joints in the articulated skeletal structure, as well as the temporal dependency due to the continuous movement of each of the joints. The spatial-temporal contexts of these joints are learnt from the sequences of joints movements and captured by our ST-HMM. Results of recognizing 11 different action classes on a large number of motion capture sequences as well as synthetic tracking data show that our approach outperforms traditional HMM approach in terms of robustness and recognition rates. © 2010 IEEE.

Research Area(s)

  • Hidden markov model, Spatial and temporal context

Citation Format(s)

Human 3D motion recognition based on spatial-temporal context of joints. / Zhao, Qiong; Wang, Lihua; Ip, Horace H. S.; Zhou, Xuehai.

Proceedings - International Conference on Pattern Recognition. 2010. p. 2740-2743 5596021.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review