Is there progress in activity progress prediction?

Frans de Boer, Jan C. van Gemert, Jouke Dijkstra, Silvia L. Pintea

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Activity progress prediction aims to estimate what percentage of an activity has been completed. Currently this is done with machine learning approaches, trained and evaluated on complicated and realistic video datasets. The videos in these datasets vary drastically in length and appearance. And some of the activities have unanticipated developments, making activity progression difficult to estimate. In this work, we examine the results obtained by existing progress prediction methods on these datasets. We find that current progress prediction methods seem not to extract useful visual information for the progress prediction task. Therefore, these methods fail to exceed simple frame-counting baselines. We design a precisely controlled dataset for activity progress prediction and on this synthetic dataset we show that the considered methods can make use of the visual information, when this directly relates to the progress prediction. We conclude that the progress prediction task is ill-posed on the currently used real-world datasets. Moreover, to fairly measure activity progression we advise to consider a, simple but effective, frame-counting baseline.
Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
EditorsCristina Ceballos
Place of PublicationPiscataway
PublisherIEEE
Pages2950-2958
Number of pages9
ISBN (Electronic)979-8-3503-0744-3
ISBN (Print)979-8-3503-0745-0
DOIs
Publication statusPublished - 2023
Event2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) - Paris, France
Duration: 2 Oct 20236 Oct 2023

Conference

Conference2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Country/TerritoryFrance
CityParis
Period2/10/236/10/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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