EdgeVidCap:用于物联网边缘摄像头的轻量级视频标题模型
Lan Guo1, Xuyang Li1, Jinqiang Wang1
1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究介绍了EdgeVidCap,一个用于物联网边缘摄像头的轻量级视频标题模型. 在资源有限的设备上实现高效的视频理解和准确的描述生成.
科学领域:
- 人工智能
- 计算机视觉
- 物联网
背景情况:
- 带有边缘计算和物联网集成的智能边缘摄像头可以实现本地视频理解.
- 现有的视频标题模型是计算密集的,阻碍了对资源有限的物联网设备的部署.
研究的目的:
- 开发一个轻量级的视频标题模型,EdgeVidCap,用于物联网边缘摄像头的高效实时处理.
- 解决当前视频标题解决方案中的高计算复杂性和大参数数量的局限性.
主要方法:
- 提出了一个协同关注状态Mamba (SASM) 模块,将频道注意力和状态空间模型 (SSM) 结合起来,以实现高效的时空特征建模.
- 开发了一种适应性注意力引导的LSTM解码器,用于自动回归的标题生成,具有动态特征加权.
- 实施了简化过机制以提高处理效率.
主要成果:
- 与MSR-VTT和MSVD数据集上现有的视频标题方法相比,EdgeVidCap显示了更高的精度.
- 由于其精简的框架过,该模型实现了更高的处理效率.
- 在选后生成更可靠的文字描述.
结论:
- EdgeVidCap为物联网边缘设备的轻量级视频标题提供了一个有效的解决方案.
- 拟议的SASM模块和自适应的LSTM解码器有助于高效准确的视频理解.
- 该模型满足资源有限的边缘计算环境的实时处理要求.
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