Research on Key Technologies of Pedestrian Detection

行人集刚性物体和柔性物体的特性于一身，受光照、姿态、穿着、遮挡和复杂背景等影响，类内和类间的模式变化大。行人检测是目标检测中的经典难题，也是当前计算机视觉的研究热点，其研究成果可应用在车辆辅助驾驶、人体运动分析、家庭服务机器人和智能视频监控等领域中。因此，开展行人检测技术的研究具有重要的理论意义和应用价值。 本文在全面综述行人检测技术研究现状的基础上，将行人检测分为如下的四个子问题：候选区域的产生、特征提取、分类与定位和验证。从模式识别的角度来看，行人检测技术的核心是“特征提取”和“分类与定位”，本文主要针对当前这两类问题的研究中存在的不足展开研究，论文的主要工作和创新点如下： （1）针对...Pedestrian has both the characteristic of rigid and non-rigid object. Due to lighting conditions, pose, clothing, partial occlusion and cluttered background, pedestrian appearance pattern has large variation in inter-class and within-class. Pedestrian detection is a classical challenging task in computer vision, and also a hot research topic since it has many useful applications such as driver ass...学位：理学博士院系专业：信息科学与技术学院智能科学与技术系_人工智能基础学号：3152008015055

Pedestrian Detection in Clutter Scene

复杂背景和遮挡问题是当前行人检测技术研究中的难点，对这两个问题的解决，有助于行人检测技术进一步实用化、市场化，创造更高的经济价值和社会价值，同时行人检测的相关技术可以被人脸检测、车辆检测等目标检测技术所借鉴，促进计算机视觉和模式识别等相关学科的发展，具有较大的理论意义和学术价值。本文针对这两个问题展开了研究。 针对复杂背景问题，本文全面分析了基于混合高斯模型运动目标检测方法的优缺点，针对其更新速度慢、收敛性差的缺点提出了相应的改进方法，通过实验验证了该改进方法的有效性；针对复杂背景中阴影对运动目标轮廓的影响，采用基于色度畸变的阴影消除方法；针对混和高斯模型在运动目标的运动方向与摄像机成像平面...Clutter background and partial occlusions are two challenging tasks of pedestrian detection in real world environments. It is important to solve these problems so that pedestrian detection can be put into practical applications. The technologies used in pedestrian detection can also be transferred to other object detection fields, such as face detection, and vehicle detection, and promote the deve...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机应用技术学号：2005130232

Convolutional neural network's image moment regularizing strategy

卷积神经网络的池化策略包含极大池化和平均池化,极大池化选择池化区域中的最大值,极易出现过抑合现象;平均池化对池化区域中所有元素赋予相同权重,降低了高频分量的权重。本文提出将矩池化作为卷积神经网络的正则化策略,矩池化将几何矩概念引入到卷积神经网络的池化过程中,首先计算池化区域的中心矩,然后根据类插值法依概率随机地从中心矩的4个邻域中选择响应值。在数据集MNIST、CIFAR10、CIFAR100上的实验结果表明随着训练迭代次数的增加,矩池化的训练误差和测试误差最低,矩池化的高差别性和强鲁棒性使其获得了比极大池化和平均池化更好的泛化能力。There are two kinds of pooling strategies for convolutional neural network( CNN) as follows: max pooling and average pooling. Max pooling simply chooses the maximum element,which makes this strategy extremely prone to overfitting. Average pooling endows all elements with the same weight,which lowers the weight of the high-frequency components. In this study,we propose moment pooling as a regularization strategy for CNN. First,we introduce the geometric moment to CNN pooling and calculate the central moment of the pooling region. Then,we randomly select the response values based on the probability-like interpolation method from the four neighbors of the moment as per their probability. Experiments on the MNIST,CIFAR10,and CIFAR100 datasets show that moment pooling obtains the fewest training and test errors with training iteration increments. This strategy's robustness and strong discrimination capability yield better generalization results than those from the max and average pooling methods.国家自然科学基金资助项目(61202143,61572409);; 福建省自然科学基金资助项目(2013J05100

Moving Object Detection for Medical Gait Analysis in Complex Scene

中文摘要：针对医学步态分析中的复杂场景下运动目标检测问题，提出了基于贝叶斯决策规则的方法。该方法由变化检测、变化 分类、前景目标提取和背景更新四部分组成。变化检测采用自适应阈值法来二值化变化点和非变化点，变化分类基于颜色共 生特征向量，采用贝叶斯规则进行决策，前景对象的提取融合了时间差分法和减背景法。针对复杂场景中背景的“渐变”和 “突变”情况，提出了不同的背景更新策略。实验表明，该方法在包含有摇动的树枝，或者灯的开关等复杂背景中能准确地 提取运动目标，因此可用在医学步态分析的研究中。英文摘要：Abstract This paper proposes a novel method for moving object detection from a video in medical gait analysis which contains not only stationary background objects but also moving background objects. It consists of four parts: change detection, change classification, foreground object abstraction, and background learning and maintenance. We use the Bayes decision rule for classification of background and foreground changes based on a special feature vector--- color co-occurrence feature. Foreground object abstraction fuse the classification results from both stationary and moving pixels. Learning strategies for the gradual and "once-off" background changes are proposed to adapt to various changes in background through the video. Extensive experiments on detecting foreground objects from a video containing waver- ing tree branches, or light open/close demonstrate that the proposed method is effective and can be used in medical gait analysis.基金项目：福建省科技重点项目资助（项目编号：2006H0037

Moving Object Detection for Medical Gait Analysis in Complex Scene

针对医学步态分析中的复杂场景下运动目标检测问题,提出了基于贝叶斯决策规则的方法。该方法由变化检测、变化分类、前景目标提取和背景更新四部分组成。变化检测采用自适应阈值法来二值化变化点和非变化点,变化分类基于颜色共生特征向量,采用贝叶斯规则进行决策,前景对象的提取融合了时间差分法和减背景法。针对复杂场景中背景的"渐变"和"突变"情况,提出了不同的背景更新策略。实验表明,该方法在包含有摇动的树枝,或者灯的开关等复杂背景中能准确地提取运动目标,因此可用在医学步态分析的研究中。This paper proposes a novel method for moving object detection from a video in medical gait analysis which contains not only stationary background objects but also moving background objects. It consists of four parts: change detection, change classification, foreground object abstraction, and background learning and maintenance. We use the Bayes decision rule for classification of background and foreground changes based on a special feature vector--- color co-occurrence feature. Foreground object abstraction fuse the classification results from both stationary and moving pixels. Learning strategies for the gradual and "once-off" background changes are proposed to adapt to various changes in background through the video. Extensive experiments on detecting foreground objects from a video containing waver- ing tree branches, or light open/close demonstrate that the proposed method is effective and can be used in medical gait analysis.福建省科技重点项目资助(项目编号:2006H0037)~

Perspective invariant binary feature descriptor based image matching algorithm

针对基于局部特征的图像匹配算法普遍存在对透视变换顽健性差的缺点,提出了一种新的二值特征描述子PIbC(PErSPECTIVE InVArIAnT bInAry COdE),提高了图像匹配算法的透视变换顽健性。首先,在提取金字塔图像fAST特征点的基础上,利用HArrIS角点响应值去除非极大值点和边缘响应点;其次,通过模拟相机不同视角成像之间的透视变换,对单个fAST特征点生成不同视角变换下图像的二值描述子,使描述子具备描述不同视角图像中同一特征点的能力。实验结果表明,算法在提高描述子透视不变性的同时时间复杂度与Surf算法近似。Current local feature based image matching algorithms are usually less robust to image perspective transformation.Aiming to solve this problem, a new perspective invariant binary code(PIBC) based image matching algorithm is proposed.Firstly, FAST corners are detected on the pyramid images, those corners with non-maximum Harris corner response value and the edge points are further eliminated.And then, by simulating the perspective transformations of images taken from different viewpoints, a single FAST corner is described with binary descriptors under different viewpoint transformations, which makes the descriptor could describe the identical feature point on different perspective transform images.Experimental results show its robustness to image perspective transformation, while its complexity is similar with SURF.国家自然科学基金资助项目(61373076;61202143); 厦门大学中央高校基金资助项目(2013121026;2011121052); 厦门大学985平台建设基金资助项目; 福建省自然科学基金资助项目(2013J05100;2010J01345;2011J01367); 厦门市科技重点基金资助项目(3502Z20123017); 高等学校博士学科点专项科研基金资助项目(201101211120024); 深圳市战略性新兴产业发展专项基金资助项目(JCYJ201206141646002

基于栈自编码器的图像分类器

图像分类问题包含两个重要的部分:特征提取器和分类器.多年来研究人员一直将精力投入到特征表示中,对于分类器却仅进行局部调参.基于一个性能优异的分类器与特征表示对图像分类系统同等重要的思想,提出了基于卷积特征的栈自编码器(stacked autoencoder on convolutional feature maps,SACF)的分类系统,并在数据集CUB-200和VGGflower上进行了实验,对比了SACF与基于卷积特征和多层感知机的卷积神经网络(CNN)分类系统的分类效果,实验结果表明SACF具有更优的分类效果.国家自然科学基金(61572409,61571188,61202143);;福建省自然科学基金(2013J05100);;中国乌龙茶产业福建省2011协同创新中心项目(闽教科[2015]75号);;福建省教育厅A类科技项目(JA13317

Human depth estimation on the basis of the sample learning method under a single camera

深度图像的研究是当前计算机视觉的研究热点。从图像中获取深度信息有2种方法:1)利用深度感应器,该方法的缺点是成本高;2)基于一个场景的多幅图像或图像序列,通过求取视差,获得深度值,该方法的缺点是需要摄像机参数,专业知识要求较高。针对上述情况,提出了一种简单有效的从单摄像头捕获的人体图像中估计出人体深度信息的方法,利用深度摄像机建立人体的“表观深度“图像对,然后对单摄像头获取的彩色图像进行人体表观特征提取,根据该表观特征检索图像对数据库,并对获得的人体深度进行估计和优化。最后,在厦门大学的深度数据库上,验证了该方法的有效性。Currently,the research on depth imaging is one of the hotspots concerning computer vision.There are two methods for acquiring depth information from images:1) The utilization of depth sensors,with the disadvantage of this method being its considerable expense.2) The utilization of multiple images or a sequence of images for the same scene by calculating the optical parallax for getting depth information,with the disadvantages of this method including the requirement of camera parameters and the need for a large amount of professional knowledge.In response to the circumstances mentioned above,this paper proposes a simple and efficient method that estimates human depth information from images captured by a single camera.The basic ideas of this method include establishing many pairs of human 'appearance depth' images by use of a depth camera,extracting human appearance features from colorful images captured by a monocular camera and then searching the image pairs database according to the appearance features,and estimating and optimizing human depth information obtained from the database of the pairs of images.Finally,simulation experimental results in the Xiamen University depth database established by ourselves were found to validate the effectiveness of the proposed method.国家自然科学基金资助项目(61202143); 福建省自然科学基金资助项目(2013J05100); 厦门市科技重点项目资助项目(3502Z20123017); 湖南省自然科学基金资助项目(12JJ2040

A New Easy Fast Camera Self-Calibration Technique

本文提出了一种新的相机自标定方法,该方法要求摄像机在3个(或3个以上)不同方位摄取一个包含其内接正三角形的圆的新型标定模板的图像。首先,从模板图像中推导得到圆环点的像点坐标;然后通过得到的圆环点像点坐标,可线性求解摄像机内参数。与传统方法不同的是,该方法避免了复杂的椭圆拟合和直线拟合,降低了计算复杂度,提高了标定速度和精度,对噪声更加鲁棒。此外,该方法中的标定过程不需要模板的任何物理度量,也不要建立模板及其图像上点的对应,标定过程简单易于操作。基于模拟和真实图像的实验验证了该方法的有效性和鲁棒性。In this paper,we present a new camera calibration approach by taking three images at least of the proposed planar pattern which includes an arbitrary circle with its inscribed regular triangle under different orientations.First,the imaged circular points are derived from the images of the proposed planar pattern.And then,the camera intrinsic matrix and extrinsic matrix are determined via the obtained imaged circular points.In contrast to the conventional methods,our method avoids ellipse and line fittings.It reduces the computing complexity and it is more robust to noise.Another advantage of our method is that neither any metric measurement on the model plane,nor the point correspondences is necessary;hence the calibration process becomes extremely simple.Experiments on both synthetic data and real image data demonstrate the robustness and effectiveness of our method.国家自然科学基金资助项目(60873179);深圳市科技计划基础研究项目(JC200903180630A

A Survey of Recognizing Action from Single Still Images

人体行为识别是计算机视觉的研究难点与热点,目前大部分研究者主要针对视频中的行为展开研究.然而,人类的视觉往往根据单张图片就可判断图片中发生的行为.基于单张静态图像的人体行为识别,挑战性更大,是近年来人体行为识别研究的一个趋势,更是探索人类视觉奥秘的一个很好切入点.本文对单张静态图像的人体行为识别方法进行梳理,将其分为三类,最后对其未来研究方向进行展望.Human action recognition is a difficult and active research area in computer vision.At present,most of researchers in this field focus on recognizing action from video.However,human can understand human action based on a single picture.Recognize action from single still images has more challenge and is a trend in action recognition in recent years,but also a good entry point to explore the mysteries of human vision.In this paper,we sort out the methods of recognizing action from single still images and classify these methods into three categories.At last,the future research directions are discussed.国家自然科学基金项目(60873179);高等学校博士学科点专项科研基金项目(20090121110032);深圳市科技计划项目-基础研究(JC200903180630A);深圳市科技研发基金项目-深港创新圈计划(ZYB200907110169A);湖南省科技厅科研项目(2010TC2006);教育厅科研项目资助(09A046