武器暴力数据集2.0:用于暴力检测的合成数据集
Muhammad Shahroz Nadeem1, Fatih Kurugollu2, Hany F Atlam3
1School of Technology, Business and Arts, University of Suffolk, Ipswich IP4 1QJ, United Kingdom.
Data in brief
|May 10, 2024
概括
研究人员使用Grand Theft Auto-V创建了第一个用于武器暴力检测的合成虚拟数据集. 这个武器暴力数据集 (WVD) 为训练真实数据稀缺的AI模型提供了一个新的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 获取用于敏感研究领域的真实数据,如暴力检测,由于道德问题和有限的访问权限而具有挑战性.
- 现有的暴力检测数据集通常依赖于非真实的来源,如电影或一般的在线视频,突出了关键的数据稀缺性.
- 需要多样化和伦理来源的数据对于训练关键领域的强大的AI模型至关重要.
研究的目的:
- 引入武器暴力数据集 (WVD),这是第一个专门用于暴力检测的合成虚拟数据集.
- 通过提供新的,伦理来源的,可扩展的数据资源来解决当前数据集的局限性.
- 为了促进深度学习模型的训练,以使用合成数据来检测暴力.
主要方法:
- 在摄影现实视频游戏"侠盗猎车手V" (GTA-V) 中生成合成暴力场景.
- 捕获的视频片段,以各种武器 (热和冷) 进行人对人战斗,从正面看.
- 创建了三个不同的类别:热暴力,冷暴力和无暴力 (控制类).
- 包括正常的RGB和光流视频,用于全面的模型训练.
主要成果:
- 枪支暴力数据集 (WVD) 成功创建,并在Kaggle上公开提供.
- 该数据集为现实世界敏感数据提供了一个受控和道德合理的替代方案.
- 合成性质允许可扩展性和增强性,以满足未来的研究需求.
结论:
- 武器暴力数据集 (WVD) 代表了解决暴力检测研究数据短缺问题的重大进展.
- 使用视频游戏合成数据生成为敏感的人工智能应用提供了可行的和道德的方法.
- 该WVD准备使研究界能够开发更有效的暴力检测模型.
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