Spatio-temporal Dynamic Texture Descriptors for Human Motion Recognition

Riccardo Mattivi, Ling Shao

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

7 Citations (Scopus)


In this chapter we apply the Local Binary Pattern on Three Orthogonal Planes (LBP-TOP) descriptor to the field of human action recognition. We modified this spatio-temporal descriptor using LBP and CS-LBP techniques combined with gradient and Gabor images. Moreover, we enhanced its performances by performing the analysis on more slices located at different time intervals or at different views. A video sequence is described as a collection of spatial-temporal words after the detection of space-time interest points and the description of the area around them. Our contribution has been in the description part, showing LBP-TOP to be 1) a promising descriptor for human action classification purposes and 2) we have developed several modifications and extensions to the descriptor in order to enhance its performance in human motion recognition, showing the method to be computationally efficient.
Original languageEnglish
Title of host publicationIntelligent Video Event Analysis and Understanding
EditorsJianguo Zhang, Ling Shao, Lei Zhang, Graeme A. Jones
Place of PublicationLondon
Number of pages252
ISBN (Print)9783642175534
Publication statusPublished - 2011

Publication series

NameStudies in Computational Intelligence
ISSN (Electronic)1860-949X


Dive into the research topics of 'Spatio-temporal Dynamic Texture Descriptors for Human Motion Recognition'. Together they form a unique fingerprint.

Cite this