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VID: A comprehensive dataset for violence detection in various contexts.

Abu Bakar Siddique Mahi1, Farhana Sultana Eshita1, Tabassum Chowdhury1

  • 1Department of Computer Science and Engineering, University of Asia Pacific, Dhaka, Bangladesh.

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A new dataset of 3020 video clips aids automated crime and violence detection systems. This balanced resource helps train and evaluate AI for faster incident identification, reducing manual review for security professionals.

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Action recognitionComputer visionDeep learningMachine learningVideo analysisVideo datasetViolence classificationViolence detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automated crime and violence detection is crucial for security professionals and law enforcement to reduce manual video analysis.
  • Existing datasets for violence detection are often limited in scope, diversity, and size, hindering the development of robust AI systems.
  • There is a need for comprehensive, balanced datasets that reflect real-world scenarios for effective training and evaluation.

Purpose of the Study:

  • To introduce a novel, balanced dataset for training and evaluating automated video analysis systems for crime and violence detection.
  • To address the limitations of existing datasets by providing a diverse and representative collection of video clips.
  • To facilitate the advancement of computer vision and machine learning models for security applications.

Main Methods:

  • Developed a dataset comprising 3020 video clips, evenly split between violent and non-violent actions.
  • Included clips with durations ranging from 3 to 12 seconds, recorded by non-professional actors.
  • Ensured a diverse range of real-world situations were captured within the dataset.

Main Results:

  • The dataset is balanced with 1510 violent and 1510 non-violent clips, offering equal representation.
  • The collection features diverse, real-world scenarios captured in short video segments.
  • Provides a valuable resource for improving the accuracy and generalizability of automated detection systems.

Conclusions:

  • The developed dataset offers a significant improvement over existing resources for automated violence detection.
  • This balanced and diverse dataset will aid in the development of more effective AI tools for security.
  • The resource supports enhanced training and evaluation, leading to better performance in real-world security applications.