Spatio-temporal covariance descriptors for action and gesture recognition

Andres Sanin, Conrad Sanderson, Mehrtash T. Harandi, Brian C. Lovell

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

122 Citations (Scopus)


We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors. The weighted projection is then exploited during boosting to create a final multiclass classification algorithm that employs the most useful spatio-temporal regions. We also show how the descriptors can be computed quickly through the use of integral video representations. Experiments on the UCF sport, CK+ facial expression and Cambridge hand gesture datasets indicate superior performance of the proposed method compared to several recent state-of-the-art techniques. The proposed method is robust and does not require additional processing of the videos, such as foreground detection, interest-point detection or tracking.

Original languageEnglish
Title of host publication2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
Number of pages8
Publication statusPublished - 4 Apr 2013
Externally publishedYes
EventIEEE Workshop on Applications of Computer Vision 2013 - Tampa, United States of America
Duration: 15 Jan 201317 Jan 2013 (Proceedings)

Publication series

NameProceedings of IEEE Workshop on Applications of Computer Vision
ISSN (Print)2158-3978
ISSN (Electronic)2158-3986


ConferenceIEEE Workshop on Applications of Computer Vision 2013
Abbreviated titleWACV 2013
Country/TerritoryUnited States of America
Internet address

Cite this