Work

A Spatio-Temporal Descriptor Based on 3D-Gradients

Year: 2008

Type: conference-paper

Abstract: In this work, we present a novel local descriptor for video sequences. The proposed descriptor is based on histograms of oriented 3D spatio-temporal gradients. Our contribution is four-fold. (i) To compute 3D gradients for arbitrary scales, we develop a memory-efficient algorithm based on integral videos. (ii) We propose a generic 3D orientation quantization which is based on regular polyhedrons. (iii) We perform an in-depth evaluation of all descriptor parameters and optimize them for action recognition. (iv) We apply our descriptor to various action datasets (KTH, Weizmann, Hollywood) and show that we outperform the state-of-the-art.

Source: British Machine Vision Conference (BMVC)

DOI: 10.5244/c.22.99

Authors: A. Klaeser, M. Marszalek, C. Schmid

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Institut national de recherche en informatique et en automatique (INRIA), Centre National de la Recherche Scientifique

OpenAlex ID: W2024868105

FWCI: 31.51

Cites: 22

Cited by: 1,868

Related to: 10