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

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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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 TwoStream 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 International Conference on Computer Vision (ICCV 2017)
Subtitle of host publicationProceedings of a meeting held 22-29 October 2017, Venice, Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)9781538610329
ISBN (Print)9781538610336
Publication statusPublished - Feb 2018
EventInternational Conference on Computer Vision (ICCV), -
Duration: 22 Oct 2017 → …

Publication series

ISSN (Print)2380-7504


ConferenceInternational Conference on Computer Vision (ICCV),
Period22/10/17 → …


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