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The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines.

Dima Damen, Hazel Doughty, Giovanni Maria Farinella

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 5, 2020
    PubMed
    Summary
    This summary is machine-generated.

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    The EPIC-KITCHENS dataset captures real-world daily activities in kitchens using egocentric video. This large-scale, annotated dataset enables advanced research in human-object interaction and action recognition.

    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Robotics

    Background:

    • The EPIC-KITCHENS dataset is the largest egocentric video benchmark for studying human-object interactions.
    • It provides a unique viewpoint on daily activities, attention, and intentions within kitchen environments.

    Purpose of the Study:

    • To detail the capture and dense annotation of the large-scale EPIC-KITCHENS dataset.
    • To introduce object, action, and anticipation challenges for egocentric video analysis.
    • To establish baselines and highlight the multimodal nature of the dataset.

    Main Methods:

    • Captured 55 hours of egocentric video from 32 participants across four countries.
    • Densely annotated videos with 39.6K action segments and 454.2K object bounding boxes.

    Related Experiment Videos

  • Utilized participant narration for intention-aware annotation and crowd-sourced ground-truths.

    • Main Results:

    • The dataset features diverse, non-scripted daily activities from participants of ten nationalities.
    • Evaluated baselines on seen and unseen kitchen splits, demonstrating dataset utility.
    • Introduced new baselines emphasizing multimodal fusion and temporal modeling for fine-grained action recognition.

    Conclusions:

    • EPIC-KITCHENS is a valuable resource for egocentric video understanding, capturing realistic human-object interactions.
    • The dataset's unique annotation and diversity facilitate research into intention and fine-grained actions.
    • Explicit temporal modeling and multimodal approaches are crucial for advancing egocentric vision tasks.