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走向更安全的环境:基于YOLO和MediaPipe的人类落检测系统,具有警报自动化
Virag Pradip Kothari1, Priti S Chakurkar2
1Student, Dr.Vishwanath Karad MIT WORLD PEACE UNIVERSITY School of Computer Science and Engineering, Kothrud, Pune, Maharashtra, India.
MethodsX
|September 29, 2025
概括
这项研究引入了一个实时落检测系统,使用姿势估计和深度学习来实现公共安全. 该系统准确地识别掉落,向当局发送即时警报.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 公共安全技术技术
背景情况:
- 人类跌倒的检测对公共安全至关重要,特别是在交通繁忙的地区,如运输码头.
- 现有系统经常在动态的现实环境中难以获得准确性和稳定性.
研究的目的:
- 开发一个精确,有效和可扩展的实时落检测系统.
- 通过先进的技术,提高大型公共区域的公共安全.
主要方法:
- 使用MediaPipe进行姿势估计和YOLOv8进行物体检测.
- 集成位置地标和活动识别算法.
- 采用异常检测技术来最大限度地减少假阳性并提高准确性.
主要成果:
- 在跌落检测中实现了96.06%的准确性和100%的回忆.
- 在动态和现实世界的场景中表现出稳健性.
- 通过Twilio成功实现了实时警报和视频录像传输.
结论:
- 开发的系统为公共安全提供了一种有效,可扩展和保护隐私的解决方案.
- 超越了现有的摔倒检测方法,证明了它的实用性和可靠性.
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