YOLO-I3D:优化膨胀的3D模型以实时识别人类活动
Ruikang Luo1, Aman Anand1, Farhana Zulkernine1
1School of Computing, Queen's University, Kingston, ON K7L 2N8, Canada.
本研究介绍了一种轻量级的双流膨胀3D (I3D) 模型,用于高效的人类活动识别 (HAR). 通过用低分辨率的RGB输入取代光流并集成YOLOv5,它提高了准确性并降低了实时应用程序的计算成本.
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:
- 人类活动识别 (HAR) 对于监测和医疗保健至关重要.
- 现有的双流模型,如膨胀3D (I3D) 是计算密集型,阻碍实时性能.
- 在I3D模型中的光流分支对高计算需求做出了重大贡献.
研究的目的:
- 为实时人类活动识别开发一个更高效的计算效率的双流模型.
- 为了解决现有的HAR方法中高计算成本的局限性.
- 为实际应用提高I3D模型的性能和效率.
主要方法:
- 提出了一个轻量级的运动信息分支,使用低分辨率的RGB输入来取代光流组件.
- 集成YOLOv5,一个高效的物体检测模型,以优化RGB分支.
- 在Kinetics-400数据集上评估了所提议的模型,称为I3D光.
主要成果:
- 与原始I3D模型相比,I3D Light模型的准确性得到了4.13%的改进.
- 通过更换光流分支,计算成本大幅降低.
- 与YOLOv5的集成进一步提高了1.42%的精度.
结论:
- 拟议的I3D Light模型为实时的人类活动识别提供了更有效的解决方案.
- 轻量级的运动处理和物体检测集成是改善HAR模型的有效策略.
- 该研究成功地平衡了实际HAR系统的准确性和计算效率.
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