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Violent actions against children.

Muhammad Alhammami1, Chee-Pun Ooi1, Wooi-Haw Tan1

  • 1Multimedia University, Malaysia.

Data in Brief
|May 17, 2017
PubMed
Summary
This summary is machine-generated.

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This paper introduces the MMU VAAC dataset, a new resource for recognizing violent actions against children. The dataset utilizes skeleton, depth, and RGB data captured with a Microsoft Kinect and a child mannequin.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Child protection is a critical societal concern.
  • Automated recognition of violent actions against children is an emerging research area.
  • Existing datasets may lack diversity or specific modalities for this task.

Purpose of the Study:

  • To introduce a novel dataset, MMU VAAC, for the specific task of recognizing violent actions against children.
  • To provide a comprehensive dataset including multiple data modalities for robust action recognition.
  • To facilitate research and development in automated child protection systems.

Main Methods:

  • The MMU VAAC dataset was created using a Microsoft Kinect sensor.
  • A child mannequin was employed during data collection to simulate actions.

Related Experiment Videos

  • The dataset comprises skeleton joint data, depth information, and RGB video streams.

    • Main Results:

    • The paper presents the MMU VAAC dataset, a new resource for violent action recognition.
    • The dataset includes synchronized skeleton, depth, and RGB data.
    • This multimodal approach enables diverse research avenues.

    Conclusions:

    • The MMU VAAC dataset offers a valuable resource for advancing research in violent action recognition against children.
    • The inclusion of multiple data modalities supports the development of more accurate and robust detection systems.
    • This dataset can contribute to the creation of enhanced safety and monitoring tools.