Robust Spatio-Temporal Features for Human Action Recognition

Riccardo Mattivi, Ling Shao

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

2 Citations (Scopus)

Abstract

In this chapter we describe and evaluate two recent feature detectors and descriptors used in the context of action recognition: 3D SIFT and 3D SURF. We first give an introduction to the algorithms in the 2D domain, named SIFT and SURF. For each method, an explanation of the theory upon which they are based is given and a comparison of the different approaches is shown. Then, we describe the extension of the 2D methods SIFT and SURF into the temporal domain, known as 3D SIFT and 3D SURF. The similarities and differences for both methods are emphasized. As a comparison of the 3D methods, we evaluate the performance of 3D SURF and 3D SIFT in the field of Human Action Recognition. Our results have shown similar accuracy performance, but a greater efficiency for 3D SURF approach compared with 3D SIFT.
Original languageEnglish
Title of host publicationMultimedia Analysis, Processing and Communications
EditorsWeisei Lin, Dacheng Tao, Janusz Kacprzyk, Zhu Li, Ebroul Izquierdo, Haohong Wang
Place of PublicationLondon
PublisherSpringer
Pages351-367
Number of pages764
Volume346
ISBN (Print)9783642195501
DOIs
Publication statusPublished - 2011

Publication series

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

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