搜索资源列表
detect.rar
- 基于OpenCV的人眼识别定位与跟踪。本例是为了实现驾驶员疲劳驾驶的识别，通过对眼睛的运动状态来判别疲劳程度,OpenCV on the human eye to identify the location and tracking, which in this case the driver is tired of driving in order to achieve the recognition, through the movement of the eyes to determine
pilaojiashi
- 在VC++环境下利用OPENCV实现人眼检测和识别，并判断驾驶员是否疲劳，自动提醒驾驶员停止驾驶。-In VC++ environment using OPENCV achieve detection and recognition of the human eye and determine whether driver fatigue, and automatically alerts the driver to stop driving.
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- 图像处理算法，防疲劳驾驶图像处理算法，防疲劳驾驶-Image processing algorithm, anti-fatigue driving image processing algorithm, anti-fatigue driving
3
- 图像处理，前端数据通过摄像头获取，后端对图像进行处理，防止疲劳驾驶-Image processing, front-end data access through the camera, the back-end image processing, to prevent fatigue driving
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- 人脸检测图像算法，定位人眼，防止疲劳驾驶-Human face detection algorithm image, positioning the human eye, to prevent fatigue driving
detection
- 通过人眼图像来检测驾驶员疲劳驾驶是目前的主流方向,面部及眼睛定位是其中关键的环节。针对驾驶员疲 劳检测系统,结合图像处理和模式识别技术,提出一种基于人脸面部特征的人眼定位方法。-Image by the human eye to detect driver fatigue, driving is the main direction, face and eye location is the key link. For the weary driver Labor inspection
EyePosition
- 利用灰度做的一个人眼跟踪程序，对疲劳驾驶的初学者有一定的帮助。-Gray made use of a human eye tracking procedures, fatigue driving, be helpful for beginners.
renlianjiance
- 已有的眼睛状态识别方法不仅计算量大，而且易受环境因素(如光照条件)的影响。为此本文提出了一种新的眼睛状态识别方法:首先对实时拍摄的图像利用运动信息和肤色特征进行人脸检测 然后在检测到的上半部人 脸区域采用Adaboost算法只检测驾驶员正常状态下睁开的眼睛，把检测 的眼睛与眉毛分割出来制成正常睁开眼睛模板 最后把眼睛模板与未检测 到眼睛的上半部人脸区域进行匹配，当该区域的最大相似度值小于某闽值 时认为眼睛是闭合状态，否则认为眼睛是睁开状态。并根据眼睛状态计算 眼睛平均闭合时间
Y1237622[1]
- 疲劳驾驶方面的论文，基于opencv。里面实现了硬件设计和详细的软件设计过程 -Fatigue driving of the paper, based on opencv. Which implements the detailed hardware design and software design process
Y1293405[1]
- 疲劳驾驶方面论文，方法简单易懂。给了详细的叙述流程-Fatigue driving paper method is simple and easy to understand. For a detailed descr iption of process
Y1381050[1]
- 疲劳驾驶方面的论文，很经典。里面用到了aboost方法-Fatigue driving of the paper, it is classic. Method which uses the aboost
detectdriverfatigue
- 通过人眼图像来检测驾驶员疲劳驾驶是目前的主流方向,面部及眼睛定位是其中关键的环节。针对驾驶员疲 劳检测系统,结合图像处理和模式识别技术,提出一种基于人脸面部特征的人眼定位方法-Images by the human eye to detect driver fatigue is the main driving direction, face and eye location is the key link. For driver fatigue detection system that
fatigue-driving-eyes-positioning
- 疲劳驾驶中眼睛的创新定位方法，更加精确的定位瞳孔-The innovation of fatigue driving eyes localization method, a more precise positioning pupil
Monitoring-drice
- 基于神经网络的防疲劳驾驶的论文，有较高的参考价值-Neural network-based anti-fatigue driving the paper, a high reference value
fatigue-driving-system
- 基于图像处理的人脸检测系统，通过openCV软件来实现的-Image processing based face detection system, implemented by software openCV
fatigue_mq_block
- 基于opencv的C语言的疲劳驾驶检测代码，肤色检测人脸-fatigue driving detecting
Digital-Algorithm
- 数字图像处理，运用眼睛定位算法用于鉴定驾驶员疲劳驾驶。-Digital image processing, using eye localization algorithm used to identify driver fatigue driving.
NLPLibSVM
- 基于安卓开发的疲劳驾驶检测系统, 可以用于识别人脸, 并判断人是否处于疲劳状态(A fatigue driving testing system based on Android, which can be used to identify faces and determine whether a person is in a state of fatigue.)
MATLAB疲劳检测系统[自己视频]
- 本设计为基于MATLAB的疲劳检测识别，可应用于疲劳驾驶监测，专注度检测等应用。本设计带有GUI可视化界面，自行录制好视频后，读取视频，分帧，读取每一帧影像，计算其眼睛张合度及嘴巴张合度，通过这2个参数来判断是否疲劳。(This design is based on MATLAB fatigue detection and recognition, can be applied to fatigue driving monitoring, concentration detection and
SVM分类
- 基于SVM的疲劳驾驶系统。基于神经网络的非接触式疲劳驾驶检测已成为当前针对疲劳驾驶检测领域炙手可热的研究方向。它有效解决了接触式疲劳检测方法给驾驶员带来的干扰以及单一信号源对于反映疲劳程度可靠性低的问题，同时通过设计神经网络模型对多源信息进行分类，实现对疲劳状态的高精度和高速度的检测。选取合适的特征值对网络检测准确率以及准确反映疲劳程度至关重要。基于驾驶员生理信号检测可靠性和准确性较高。(Fatigue driving system based on SVM)