用红外摄像头+树莓派实现黑暗环境下的手势识别与控制
Extraction Of Cumulative Blobs From Dynamic Gestures
- 通过红外摄像头捕捉动态手势，克服光照限制
- 基于OpenCV和机器学习算法识别手势轨迹并控制GPIO
- 适合无光环境的交互系统开发，如智能家居控制
手势识别是一种基于计算机视觉技术的感知用户界面，可将人类动作解读为指令，实现无需手部操作的计算机交互。其主要弱点在于对光照条件敏感，因依赖摄像头进行2D/3D手势解析，光照变化会影响识别效果，导致暗环境无法使用。本研究采用带红外补光的夜视摄像头，通过移除红外滤镜使相机能接收不可见的红外光，在黑暗环境中仍可清晰捕捉运动轨迹。视频流输入树莓派，运行搭载OpenCV的Python程序，完成手势检测、分离与路径追踪；随后利用机器学习算法识别绘制模式，并据此控制树莓派的GPIO引脚执行相应操作。
原文摘要 · Abstract (English)
Gesture recognition is a perceptual user interface, which is based on CV technology that allows the computer to interpret human motions as commands, allowing users to communicate with a computer without the use of hands, thus making the mouse and keyboard superfluous. Gesture recognition's main weakness is a light condition because gesture control is based on computer vision, which heavily relies on cameras. These cameras are used to interpret gestures in 2D and 3D, so the extracted information can vary depending on the source of light. The limitation of the system cannot work in a dark environment. A simple night vision camera can be used as our camera for motion capture as they also blast out infrared light which is not visible to humans but can be clearly seen with a camera that has no infrared filter this majorly overcomes the limitation of systems which cannot work in a dark environment. So, the video stream from the camera is fed into a Raspberry Pi which has a Python program running OpenCV module which is used for detecting, isolating and tracking the path of dynamic gesture, then we use an algorithm of machine learning to recognize the pattern drawn and accordingly control the GPIOs of the raspberry pi to perform some activities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。