用手指空中绘画，100毫秒延迟，手部检测提升识别精度
An Approach for Air Drawing Using Background Subtraction and Contour Extraction
- 先建背景图，再减去实时画面得二值图，找轮廓顶点定位笔尖
- 实测空气绘图延迟仅100毫秒，支持画圈模拟书写
- 结合Haar级联检测手部，避免误检，适合手势交互应用
本文提出一种基于图像处理的空中绘画新方法，通过移动手指在空中绘制内容并投射到屏幕上。首先在初始摄像头帧中取均值构建感兴趣区域（ROI）背景图，再与实时画面相减生成二值掩码图。通过提取二值图中轮廓的顶部点确定指针位置，并在画布上以该位置画圆来模拟书写过程。为减少误检，引入预训练的Haar级联分类器进行手部检测，优化背景差分效果。实验显示系统端到端延迟仅为100毫秒。相关代码已开源至GitHub：https://github.com/q-viper/Contour-Based-Writing。
原文摘要 · Abstract (English)
In this paper, we propose a novel approach for air drawing that uses image processing techniques to draw on the screen by moving fingers in the air. This approach benefits a wide range of applications such as sign language, in-air drawing, and 'writing' in the air as a new way of input. The approach starts with preparing ROI (Region of Interest) background images by taking a running average in initial camera frames and later subtracting it from the live camera frames to get a binary mask image. We calculate the pointer's position as the top of the contour on the binary image. When drawing a circle on the canvas in that position, it simulates the drawing. Furthermore, we combine the pre-trained Tesseract model for OCR purposes. To address the false contours, we perform hand detection based on the haar cascade before performing the background subtraction. In an experimental setup, we achieved a latency of only 100ms in air drawing. The code used to this research are available in GitHub as https://github.com/q-viper/Contour-Based-Writing
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