Authors:
Yingying Liu
and
Arcot Sowmya
Affiliation:
The University of New South Wales, Australia
Keyword(s):
Action Recognition, Action Description, Temporal Pyramid Histograms.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Information Retrieval and Learning
;
Learning of Action Patterns
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Theory and Methods
;
Video Analysis
Abstract:
In this paper, we present an approach to action description based on temporal pyramid histograms. Bag of
features is a widely used action recognition framework based on local features, for example spatio-temporal
feature points. Although it outperforms other approaches on several public datasets, sequencing information
is ignored. Instead of only calculating the occurrence of code words, we also encode their temporal layout in
this work. The proposed temporal pyramid histograms descriptor is a set of histogram atoms generated from
the original video clip and its subsequences. To classify actions based on the temporal pyramid histograms
descriptor, we design a function to calculate the weights of the histogram atoms according to the corresponding
sequence lengths. We test the descriptor using nearest neighbour for classification. Experimental results show
that, in comparison to the state-of-the-art, our description approach improves action recognition accuracy.