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MATLAB疲劳检测专注度识别GUI用户界面

于 2020-08-07 发布
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下载积分: 1 下载次数: 16

代码说明:

说明:  一、主要研究内容 1.基本内容 本设计目标在于利用Matlab强大的图像处理能力和实用便捷的编程方法,通过处理包含人脸的视频帧系列图像,灰度积分投影技术的眼睛定位方法,进而利用perclos计数,计算眨眼率,从而得到比较准确的疲劳状况。包括lun文初稿撰写。 2.具体要求 本设计基于灰度积分投影技术的眼睛定位方法,再结合perclos技术。首先通过图像预处理技术得到灰度分配较为均匀的图像,然后分别利用水平和垂直灰度积分投影曲线结合人脸的结构特征找到眼睛的位置坐标,实现了准确的眼睛定位,通过perclos技术技术眨眼率,根据先验值得到是否疲劳。 二、算法原理 视频输入——预处理去除非人脸区域——人脸定位——人眼定位(灰度积分投影)——眼睛张合度——perclos技术统计——输出结果 三、GUI界面设计 [需要有一定编程功底](1. Basic contents The goal of this design is to use the powerful image processing ability of MATLAB and the practical and convenient programming method, through processing the video frame series images including face, the eye location method of gray integral projection technology, and then using PERCLOS count to calculate the blink rate, so as to get a more accurate fatigue condition. Including the first draft of Lun.)

文件列表:

MATLAB疲劳检测专注度识别GUI用户界面, 0 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\Eye_jingzhundingwei.m, 1485 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\GUI.fig, 19006 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\GUI.m, 9298 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\GUI界面.png, 117846 , 2020-02-04
MATLAB疲劳检测专注度识别GUI用户界面\GUI预期图.png, 315763 , 2020-02-04
MATLAB疲劳检测专注度识别GUI用户界面\GetAllDatabase.m, 134 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\GetEyeAccurateDatabase.m, 982 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\GetFaceDatabase.m, 699 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\GetValideDatabase.m, 721 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\Nv.mat, 381 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\ResultOut.m, 469 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\video_images, 0 , 2020-07-30
MATLAB疲劳检测专注度识别GUI用户界面\video_images\01.jpg, 135020 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\02.jpg, 134274 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\03.jpg, 134216 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\04.jpg, 133838 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\05.jpg, 133797 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\06.jpg, 134118 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\07.jpg, 133724 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\08.jpg, 136020 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\09.jpg, 137124 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\10.jpg, 136509 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\11.jpg, 136076 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\12.jpg, 135725 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\13.jpg, 135259 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\14.jpg, 135185 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\15.jpg, 135742 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\16.jpg, 133797 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\17.jpg, 134118 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\18.jpg, 134216 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\19.jpg, 134274 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\20.jpg, 137124 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\21.jpg, 135725 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\22.jpg, 135020 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\23.jpg, 135742 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\24.jpg, 137124 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\25.jpg, 135020 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\26.jpg, 133724 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\27.jpg, 135725 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\28.jpg, 136076 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\29.jpg, 136076 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\video_images\30.jpg, 134216 , 2018-05-05
MATLAB疲劳检测专注度识别GUI用户界面\youxiaoquyu.m, 807 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\人眼定位.png, 78461 , 2020-02-04
MATLAB疲劳检测专注度识别GUI用户界面\原理图.jpg, 87939 , 2008-05-03
MATLAB疲劳检测专注度识别GUI用户界面\打开方式.txt, 572 , 2020-07-31
MATLAB疲劳检测专注度识别GUI用户界面\灰度积分投影人眼定位.png, 155395 , 2020-02-04
MATLAB疲劳检测专注度识别GUI用户界面\预处理.png, 184977 , 2020-02-04

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  • imu
    六轴陀螺仪校正算法,有详细的例子。本人已测试效果不错。(Six-axis gyroscope correction algorithm, a detailed example. I have test results are good.)
    2016-04-14 10:26:48下载
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    曲面拟合法是一种高精度、计算量小、抗噪性能强的方法,当前已被广泛地应用于 各种场景。比如岩石材料的变形测量、电子器件变形的测量、基于掌纹图像相关匹 配的重复定位技术等。曲面拟合法能够成功计算出亚像素位移的前提是整像素搜索阶 段可以正确搜索出模板的整像素匹配点,一旦整像素匹配点搜索出现错误,那么亚像素 测量阶段所得到的位移距离就会失去意义。 使用不同的拟合函数对相关系数矩阵进行曲面拟合,会对结果产生一定的影响,比 较常用的拟合函数有二次函数、三次函数、高斯函数等。(Surface fitting method is a high-precision, computational complexity is small, strong anti-noise performance method, has been widely used in various scenes. For example, deformation measurement of rock materials, deformation measurement of electronic devices, and repetitive location technology based on palmprint image correlation matching. Surface fitting method can successfully calculate the sub-pixel displacement on the premise that the whole pixel search stage can correctly search the template of the whole pixel matching point, once the whole pixel matching point search error, then the sub-pixel measurement phase of the displacement distance will be meaningless. Using different fitting functions to fit the surface of the correlation coefficient matrix will have a certain impact on the results. The commonly used fitting functions are quadratic function, cubic function, Gaussian function and so on.)
    2018-09-26 21:46:28下载
    积分:1
  • brenner
    对于离焦图像进行自动对焦的图像判定,通过对一些列的图像,从离焦到对焦再到离焦进行图像分析从而获得对焦图像的图片。(For the defocus image autofocus image determination, through a series of images into focus and then from defocus defocused image analysis to obtain a focused image of the picture.)
    2013-07-19 20:10:33下载
    积分:1
  • SSC_1.0
    一种用于稀疏表示求解L1范数的算法,用L1范数近似L0范数获得了更好的效果。(A method for sparse representation algorithm for solving the L1-norm)
    2013-07-19 19:19:46下载
    积分:1
  • HOG
    读取一24位彩色图像,计算其以下图像特征: 1)灰度直方图 2)颜色直方图(RGB3个通道) 3)方向梯度直方图(HOG,将图像分成4*4 16个块,8个梯度方向) (Read a 24-bit color images, calculate the following image features: 1) Histogram 2) color histogram (RGB3 channels) 3) histogram of oriented gradients (HOG, the image is divided into 4* 4 16 blocks, 8 gradient directions))
    2020-06-28 15:00:01下载
    积分:1
  • com
    图像处理算法,实现多种对于灰度/二值图像的基本算法,包括灰度直方图,均衡化,门限处理,模糊,锐化,腐蚀,膨胀,掩模过滤,频域转换,边缘检测,梯度检测,二值化等等.实现的方法较多,且均经过一定的优化,效率不错.(Image processing algorithms to achieve a variety of for the gray/binary image of the basic algorithm, including the gray histogram, equalization, threshold treatment, blur, sharpen, corrosion, expansion, mask filter, frequency domain conversion, edge detection, gradient detection, binarization and so forth. The way to achieve more, and have to undergo a certain degree of optimization, efficiency, good.)
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    2016-11-28 21:05:34下载
    积分:1
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    积分:1
  • mD
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