融合对象检测和动作识别,以预测暴力行动.
Nelson R P Rodrigues1,2,3, Nuno M C da Costa2,3,4, César Melo2,3
1Engineering School, University of Minho, 4800-058 Guimarães, Portugal.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了共享自动驾驶汽车的深度学习融合监控系统,可以实时检测暴力行为,物体和丢失的物品,以提高安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 汽车安全 汽车安全
背景情况:
- 对共享自动驾驶汽车 (SAV) 的内部监控对于乘客的安全和保障至关重要.
- 现有的系统缺乏对各种机事件的全面实时监控能力.
研究的目的:
- 开发和评估基于深度学习的SAV内部的聚变监测解决方案.
- 整合用于检测暴力行为,物体和丢失物品的系统.
主要方法:
- 利用公共数据集 (COCO,TAO) 来训练像YOLOv5.5这样的物体检测模型.
- 使用了MoLa InCar数据集用于培训行动认可模型 (I3D,R(2+1) D,SlowFast,TSN,TSM).
- 实现了嵌入式汽车解决方案,以验证实时性能.
主要成果:
- 成功训练了最先进的深度学习模型,用于特定的机监控任务.
- 证明了能够在汽车环境中实时操作的融合系统的可行性.
结论:
- 深度学习提供了一种可行的方法,用于在SAV中进行全面的客内监控.
- 拟议的融合系统通过实时检测关键事件来提高安全性和安全性.
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