Authors:
Kahraman Ayyildiz
and
Stefan Conrad
Affiliation:
Heinrich Heine University Duesseldorf, Germany
Keyword(s):
Motion Detection, Motion Recognition, Action Recognition, Repeating Movement, Video Classification, Frequency Feature, Invariance, View-invariant.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Segmentation and Grouping
Abstract:
This paper discusses an approach, which allows classifying videos by frequency spectra. Many videos contain activities with repeating movements. Sports videos, home improvement videos, or videos showing mechanical motion are some example areas. Motion of these areas usually repeats with a certain main frequency and several side frequencies. Transforming repeating motion to its frequency domain via FFT reveals these frequency features. In this paper we explain how to compute frequency features for video clips and how to use them for classifying. The experimental stage of this work focuses on the invariance of these features with respect to rotation, reflection, scaling, translation and time shift.