Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video

Davide Moltisanti, Michael Wray, Walterio Mayol-Cuevas, Dima Damen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Manual annotations of temporal bounds for object interactions (i.e. start and end times) are typical training input to recognition, localization and detection algorithms. For three publicly available egocentric datasets, we uncover inconsistencies in ground truth temporal bounds within and across annotators and datasets. We systematically assess the robustness of state-of-the-art approaches to changes in labeled temporal bounds, for object interaction recognition. As boundaries are trespassed, a drop of up to 10% is observed for both Improved Dense Trajectories and Two- Stream Convolutional Neural Network. We demonstrate that such disagreement stems from a limited understanding of the distinct phases of an action, and propose annotating based on the Rubicon Boundaries, inspired by a similarly named cognitive model, for consistent temporal bounds of object interactions. Evaluated on a public dataset, we report a 4% increase in overall accuracy, and an increase in accuracy for 55% of classes when Rubicon Boundaries are used for temporal annotations.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Computer Vision (ICCV)
PublisherIEEE
Pages2905-2913
Number of pages9
ISBN (Electronic)978-1-5386-1032-9
ISBN (Print)978-1-5386-1033-6
DOIs
Publication statusPublished - 1 Oct 2017
Event2017 IEEE International Conference on Computer Vision - Venice, Italy
Duration: 22 Oct 201729 Oct 2017
http://iccv2017.thecvf.com/

Publication series

NameInternational Conference on Computer Vision (ICCV)
PublisherIEEE
ISSN (Electronic)2380-7504

Conference

Conference2017 IEEE International Conference on Computer Vision
Abbreviated titleICCV 2017
Country/TerritoryItaly
CityVenice
Period22/10/1729/10/17
Internet address

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