Human activity recognition based on ℜ transform

Ying Wang*, Kaiqi Huang, Tieniu Tan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

126 Citations (Scopus)

Abstract

This paper addresses human activity recognition based on a new feature descriptor. For a binary human silhouette, an extended radon transform, ℜ transform, is employed to represent low-level features. The advantage of the ℜ transform lies in its low computational complexity and geometric invariance. Then a set of HMMs based on the extracted features are trained to recognize activities. Compared with other commonly-used feature descriptors, ℜ transform is robust to frame loss in video, disjoint silhouettes and holes in the shape, and thus achieves better performance in recognizing similar activities. Rich experiments have proved the efficiency of the proposed method.

Original languageEnglish
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Place of PublicationPiscataway, N.J
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-8
Number of pages8
ISBN (Print)1424411807, 9781424411801, 1424411793
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: 17 Jun 200722 Jun 2007

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period17/06/0722/06/07

Fingerprint Dive into the research topics of 'Human activity recognition based on ℜ transform'. Together they form a unique fingerprint.

Cite this