Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

We present a novel unsupervised learning method for human action categories. A video sequence is represented as a collection of spatial-temporal words by extracting space-time interest points. The algorithm automatically learns the probability distributions of the spatial-temporal words and the inte...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computer vision 2008-09, Vol.79 (3), p.299-318
Main Authors: Niebles, Juan Carlos, Wang, Hongcheng, Fei-Fei, Li
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Computer Imaging
Computer Science
Computer science
control theory
systems
Datasets
Exact sciences and technology
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Pattern recognition. Digital image processing. Computational geometry
Speech and sound recognition and synthesis. Linguistics
Studies
Vision
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present a novel unsupervised learning method for human action categories. A video sequence is represented as a collection of spatial-temporal words by extracting space-time interest points. The algorithm automatically learns the probability distributions of the spatial-temporal words and the intermediate topics corresponding to human action categories. This is achieved by using latent topic models such as the probabilistic Latent Semantic Analysis (pLSA) model and Latent Dirichlet Allocation (LDA). Our approach can handle noisy feature points arisen from dynamic background and moving cameras due to the application of the probabilistic models. Given a novel video sequence, the algorithm can categorize and localize the human action(s) contained in the video. We test our algorithm on three challenging datasets: the KTH human motion dataset, the Weizmann human action dataset, and a recent dataset of figure skating actions. Our results reflect the promise of such a simple approach. In addition, our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-007-0122-4