视频图像中的运动人体检测和人脸识别
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摘要
本论文主要介绍了作者对运动目标检测与分析中一些算法的研究,主要包括了视频序列中运动目标的分割算法与外轮廓精确提取算法、非拥挤环境中的运动人体检测算法、视频序列中具有可分离性的多人脸检测算法、多角度不同表情下的人脸识别算法等。本文的目的是研究简单高效的算法,将运动人体的检测与分割、人脸检测与识别作为一系列的问题进行研究。本论文的主要研究成果列举如下:
在研究已有算法的基础上,针对基于帧间差分方法对噪声敏感的问题,本文提出一种基于特征对象的的运动目标检测与外轮廓精确提取算法。特征对象由改进的KL变换计算得出,它不但具有运动对象的位置、形状等信息,而且与原图像序列的某一帧图像相对应。由此,可以进行运动对象的分割与提取。对刚体和非刚体运动对象,利用特征对象方法都只需3帧即可将其与背景分离。检测到特征对象后,将时间信息与空间信息相结合提取运动目标的精确位置及外轮廓。进一步,设计了基于KLT/Snake混合模型的运动目标外轮廓精确提取算法。实验结果表明,该算法计算速度快,能够正确的检测与分割复杂背景下的多运动目标,并且可精确提取运动目标的外轮廓。
在研究现有人体检测算法的基础上,以简单高效为原则,设计了基于时间信息和人体形状信息相结合的非拥挤环境下人体检测算法。首先利用本文提出的特征对象法检测和分割运动目标,然后利用人体的形状信息区分运动目标中的人体与非人体,并利用连续多帧排除了目标间的短时互遮挡。算法的优点是运动目标检测准确,进行人体检测时不受人体角度的影响。而且,由于在运动目标检测时已对目标阴影进行了处理,因此提取的人体外形不受阴影的影响。分析和实验表明,该算法抗干扰能力强,可以准确检测到非拥挤场景中的多个运动人体。
提出了将运动信息与边缘投影函数相结合的视频序列人脸检测与定位算法;针对经典Sobel算子检测到的边缘粗,对噪声敏感的问题,设计了双阈值Sobel算子进行边缘检测,该算子检测到的图像边缘清晰、细致、噪声少;提出了平方投影函数,该投影函数不但可区分均值相同的区域,而且可区分方差相同的区域。因为边缘图像携带了丰富的图像信息,且对光照条件的改变不敏感;投影函数可检测灰度值变化的区域;运动目标检测得到的图像背景简单,因此将运动信息、边缘函数与投影函数结合起来设计的人脸检测与定位算法简单实用。
多角度不同表情下的人脸识别是人脸识别领域的一个难点。设计人脸识别算法时最重要的两个步骤一是设计合适的特征提取算法,另一个是设计合适的后期
This dissertation describes our investigation on some algorithm of computer vision. Research fruits mainly include segmentation algorithm and precise outline segmentation algorithm of moving objects from sequential images, detection algorithm of moving human body from un-crowding scene, faces detection algorithm from video sequences, face recognition under different expressions and multi-views etc. The motivation of this dissertation is how to find simple and effective algorithms, so as to combine the moving body detection, face detection and recognition as a series problem. The contributions of this dissertation are listed below.
Aiming at existing defects of traditional segmentation by temporal differencing method, such as sensitive to noise, sensitive to the dynamic background etc, we suggest a new moving object segmentation algorithm based on eigenobject. Eigenobject is computed by improved KL transform. It not only contains the location information and the shape information of the moving objects, but also corresponds to the middle frame of the original image sequence. The moving object can be extracted by using eigenobject. In this step, three frames are enough for rigid and nonrigid object. Furthermore, a moving object outline precise extracting algorithm is designed based on a KLT/Snake model. The experimental results show that, the algorithm proposed in the paper has a high speed and a definite meaning; it can detect and segment the moving objects accurately from complex background.
On the basis of studying prior work, a human body detection algorithm based on the combination of temporal information and shape information is designed. Firstly, moving objects are detected using the proposed segmentation algorithm. Secondly, shape information is used to distinguish human body and other moving object. Furthermore, occlusion during a short time is handled by detecting the shape of moving object in continues frames. The algorithm has the advantages of accurately moving object detection and the detection result don’t effect by the body pose. Experiment results show that the algorithm is robust to noise and can detect the human bodies under complex circumstance.
A face detection algorithm combined moving information with edge projection function is proposed. Aiming at existing defaults of traditional Sobel operator such as sensitive to noise and the detected edges are to thick, a two threshold Sobel operator is
在研究已有算法的基础上,针对基于帧间差分方法对噪声敏感的问题,本文提出一种基于特征对象的的运动目标检测与外轮廓精确提取算法。特征对象由改进的KL变换计算得出,它不但具有运动对象的位置、形状等信息,而且与原图像序列的某一帧图像相对应。由此,可以进行运动对象的分割与提取。对刚体和非刚体运动对象,利用特征对象方法都只需3帧即可将其与背景分离。检测到特征对象后,将时间信息与空间信息相结合提取运动目标的精确位置及外轮廓。进一步,设计了基于KLT/Snake混合模型的运动目标外轮廓精确提取算法。实验结果表明,该算法计算速度快,能够正确的检测与分割复杂背景下的多运动目标,并且可精确提取运动目标的外轮廓。
在研究现有人体检测算法的基础上,以简单高效为原则,设计了基于时间信息和人体形状信息相结合的非拥挤环境下人体检测算法。首先利用本文提出的特征对象法检测和分割运动目标,然后利用人体的形状信息区分运动目标中的人体与非人体,并利用连续多帧排除了目标间的短时互遮挡。算法的优点是运动目标检测准确,进行人体检测时不受人体角度的影响。而且,由于在运动目标检测时已对目标阴影进行了处理,因此提取的人体外形不受阴影的影响。分析和实验表明,该算法抗干扰能力强,可以准确检测到非拥挤场景中的多个运动人体。
提出了将运动信息与边缘投影函数相结合的视频序列人脸检测与定位算法;针对经典Sobel算子检测到的边缘粗,对噪声敏感的问题,设计了双阈值Sobel算子进行边缘检测,该算子检测到的图像边缘清晰、细致、噪声少;提出了平方投影函数,该投影函数不但可区分均值相同的区域,而且可区分方差相同的区域。因为边缘图像携带了丰富的图像信息,且对光照条件的改变不敏感;投影函数可检测灰度值变化的区域;运动目标检测得到的图像背景简单,因此将运动信息、边缘函数与投影函数结合起来设计的人脸检测与定位算法简单实用。
多角度不同表情下的人脸识别是人脸识别领域的一个难点。设计人脸识别算法时最重要的两个步骤一是设计合适的特征提取算法,另一个是设计合适的后期
This dissertation describes our investigation on some algorithm of computer vision. Research fruits mainly include segmentation algorithm and precise outline segmentation algorithm of moving objects from sequential images, detection algorithm of moving human body from un-crowding scene, faces detection algorithm from video sequences, face recognition under different expressions and multi-views etc. The motivation of this dissertation is how to find simple and effective algorithms, so as to combine the moving body detection, face detection and recognition as a series problem. The contributions of this dissertation are listed below.
Aiming at existing defects of traditional segmentation by temporal differencing method, such as sensitive to noise, sensitive to the dynamic background etc, we suggest a new moving object segmentation algorithm based on eigenobject. Eigenobject is computed by improved KL transform. It not only contains the location information and the shape information of the moving objects, but also corresponds to the middle frame of the original image sequence. The moving object can be extracted by using eigenobject. In this step, three frames are enough for rigid and nonrigid object. Furthermore, a moving object outline precise extracting algorithm is designed based on a KLT/Snake model. The experimental results show that, the algorithm proposed in the paper has a high speed and a definite meaning; it can detect and segment the moving objects accurately from complex background.
On the basis of studying prior work, a human body detection algorithm based on the combination of temporal information and shape information is designed. Firstly, moving objects are detected using the proposed segmentation algorithm. Secondly, shape information is used to distinguish human body and other moving object. Furthermore, occlusion during a short time is handled by detecting the shape of moving object in continues frames. The algorithm has the advantages of accurately moving object detection and the detection result don’t effect by the body pose. Experiment results show that the algorithm is robust to noise and can detect the human bodies under complex circumstance.
A face detection algorithm combined moving information with edge projection function is proposed. Aiming at existing defaults of traditional Sobel operator such as sensitive to noise and the detected edges are to thick, a two threshold Sobel operator is
引文
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