为在驾驶员佩戴眼镜的情况下也能准确有效地检测疲劳状态,提出一种判断是否佩戴眼镜的方法,并建立了基于眼睛与嘴部状态的疲劳驾驶检测系统。对该系统中有关目标检测、特征提取与图像识别等算法进行研究。首先,采用Adaboost算法通过人...为在驾驶员佩戴眼镜的情况下也能准确有效地检测疲劳状态,提出一种判断是否佩戴眼镜的方法,并建立了基于眼睛与嘴部状态的疲劳驾驶检测系统。对该系统中有关目标检测、特征提取与图像识别等算法进行研究。首先,采用Adaboost算法通过人脸分类器从视频帧中检测人脸区域,并根据面部器官几何分布规则粗检眼睛与嘴部区域;其次,基于大律法自适应二值化,采用垂直积分投影法判断是否配戴眼镜,根据灰度直方图统计特征值法判断戴眼镜的眼部区域状态,另外,利用似圆度判断嘴部打哈欠情况;最后,利用PERCLOS(Percentage of Eyelid Closure over the Pupil)值识别眼睛疲劳状态,利用打哈欠频率识别嘴部疲劳状态。当检测到驾驶员处于疲劳状态,则及时给出疲劳警告。实验结果表明,该方法可有效解决眼镜对检测的干扰,并适用于不同光照与环境。同时,在戴眼镜情况下对于眼睛与嘴部疲劳状态的判断优于其他方法。基本满足疲劳检测系统对良好的实时性、稳定性与鲁棒性等要求。展开更多
提出一种采用图像直方图特征(Histogram Feature,HF)函数的自动曝光方法,用于在背景光照快速、大范围变化的情况下对高速相机进行自动曝光控制。首先,采用多点测光对获取的图像进行兴趣区域(Region of Interests,ROIs)提取,以降低系统...提出一种采用图像直方图特征(Histogram Feature,HF)函数的自动曝光方法,用于在背景光照快速、大范围变化的情况下对高速相机进行自动曝光控制。首先,采用多点测光对获取的图像进行兴趣区域(Region of Interests,ROIs)提取,以降低系统测光的计算量。然后,通过计算兴趣区域的HF函数选取大步长对曝光时间进行粗调。最后,通过模糊逻辑计算出曝光时间的精调步长并运用阈值限制实现变步长搜索最佳曝光时间,提高高速相机自动曝光的准确性及稳定性。实验结果表明:本文方法可以在2ms内完成一帧图像的亮度测量并对曝光时间进行调整,相较于基于平均亮度值的传统自动曝光方法,在0~110ms内,光源光照强度从760~23 100lux反复变化时,本文方法获得的图像信息熵比平均亮度值法的信息熵均值提高48.38%,方差降低62.13%,可以提供更好与更稳定的图片细节信息,为后续的自动调焦、图像识别以及目标跟踪提供参考。展开更多
The histogram of oriented gradient has been successfully applied in many research fields with excellent performance especially in pedestrian detection. However, the method has rarely been applied to face recognition. ...The histogram of oriented gradient has been successfully applied in many research fields with excellent performance especially in pedestrian detection. However, the method has rarely been applied to face recognition. Aimed to develop a fast and efficient new feature for face recognition, the original HOG and its variations were applied to evaluate the effects of different factors. An information theory-based criterion was also developed to evaluate the potential classification power of different features. Comparative experiments show that even with a relatively simple feature descriptor, the proposed HOG feature achieves almost the same recognition rate with much lower computational time than the widely used Gabor feature on the FRGC and CAS-PEAL databases.展开更多
文摘为在驾驶员佩戴眼镜的情况下也能准确有效地检测疲劳状态,提出一种判断是否佩戴眼镜的方法,并建立了基于眼睛与嘴部状态的疲劳驾驶检测系统。对该系统中有关目标检测、特征提取与图像识别等算法进行研究。首先,采用Adaboost算法通过人脸分类器从视频帧中检测人脸区域,并根据面部器官几何分布规则粗检眼睛与嘴部区域;其次,基于大律法自适应二值化,采用垂直积分投影法判断是否配戴眼镜,根据灰度直方图统计特征值法判断戴眼镜的眼部区域状态,另外,利用似圆度判断嘴部打哈欠情况;最后,利用PERCLOS(Percentage of Eyelid Closure over the Pupil)值识别眼睛疲劳状态,利用打哈欠频率识别嘴部疲劳状态。当检测到驾驶员处于疲劳状态,则及时给出疲劳警告。实验结果表明,该方法可有效解决眼镜对检测的干扰,并适用于不同光照与环境。同时,在戴眼镜情况下对于眼睛与嘴部疲劳状态的判断优于其他方法。基本满足疲劳检测系统对良好的实时性、稳定性与鲁棒性等要求。
文摘提出一种采用图像直方图特征(Histogram Feature,HF)函数的自动曝光方法,用于在背景光照快速、大范围变化的情况下对高速相机进行自动曝光控制。首先,采用多点测光对获取的图像进行兴趣区域(Region of Interests,ROIs)提取,以降低系统测光的计算量。然后,通过计算兴趣区域的HF函数选取大步长对曝光时间进行粗调。最后,通过模糊逻辑计算出曝光时间的精调步长并运用阈值限制实现变步长搜索最佳曝光时间,提高高速相机自动曝光的准确性及稳定性。实验结果表明:本文方法可以在2ms内完成一帧图像的亮度测量并对曝光时间进行调整,相较于基于平均亮度值的传统自动曝光方法,在0~110ms内,光源光照强度从760~23 100lux反复变化时,本文方法获得的图像信息熵比平均亮度值法的信息熵均值提高48.38%,方差降低62.13%,可以提供更好与更稳定的图片细节信息,为后续的自动调焦、图像识别以及目标跟踪提供参考。
基金Supported by the National Key Basic Research and Development(973) Program of China (No. 2007CB311004)the National High-Tech Research and Development (863) Program of China(No. 2006AA01Z115)
文摘The histogram of oriented gradient has been successfully applied in many research fields with excellent performance especially in pedestrian detection. However, the method has rarely been applied to face recognition. Aimed to develop a fast and efficient new feature for face recognition, the original HOG and its variations were applied to evaluate the effects of different factors. An information theory-based criterion was also developed to evaluate the potential classification power of different features. Comparative experiments show that even with a relatively simple feature descriptor, the proposed HOG feature achieves almost the same recognition rate with much lower computational time than the widely used Gabor feature on the FRGC and CAS-PEAL databases.
