Live repetition counting

Ofir Levy, Lior Wolf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The task of counting the number of repetitions of approximately the same action in an input video sequence is addressed. The proposed method runs online and not on the complete pre-captured video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a convolutional neural network and the information is then integrated over time. The entropy of the network's predictions is used in order to automatically start and stop the repetition counter and to select the appropriate time scale. Coupled with a region of interest detection mechanism, the method is robust enough to handle real world videos, even when the camera is moving. A unique property of our method is that it is shown to successfully train on entirely unrealistic data created by synthesizing moving random patches.

Original languageEnglish
Title of host publication2015 International Conference on Computer Vision, ICCV 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3020-3028
Number of pages9
ISBN (Electronic)9781467383912
DOIs
StatePublished - 17 Feb 2015
Event15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile
Duration: 11 Dec 201518 Dec 2015

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2015 International Conference on Computer Vision, ICCV 2015
ISSN (Print)1550-5499

Conference

Conference15th IEEE International Conference on Computer Vision, ICCV 2015
Country/TerritoryChile
CitySantiago
Period11/12/1518/12/15

Fingerprint

Dive into the research topics of 'Live repetition counting'. Together they form a unique fingerprint.

Cite this