基于Boosted Cascade算法的人脸检测和跟踪系统

将基于Boosted Cascade的人脸检测算法运用到视频图像当中,并结合图像序列中的运动信息,提出并实现了一种实时的人脸检测跟踪系统.首先根据图像的运动信息提取出可能存在人脸的候选区域,然后在候选区域中用Boosted Cascade算法进行检测.实验结果表明该系统能够实时地对于人脸进行检测跟踪,可以被应用在智能视频监控方面.     

基于区域特征的视频序列人脸快速检测

提出了一种基于区域特征的快速人脸检测算法.采用瞬时差分和背景差分获取并跟踪运动目标.消除了运动目标引起的背景模型更新误差.在检测到的运动目标区域内.通过基于区域特征的马赛克三分图模型检测人脸区域,并利用频率直方图方法合并所检测区域,最终获得人脸位置.实验结果表明,平均检测时间为30ms/帧,检测准确率可达95.7%,算法复杂度低、检测效果好,适合各类视频图像的人脸实时检测.     

基于人脸检测和多线索融合的实时人脸跟踪系统

在分析和总结国内外对人脸检测与跟踪的相关研究成果的基础上,研究提出了在Viola-Jones人脸检测算法中引入运动区域检测和人脸肤色检测以缩小人脸检测的搜索区域,有效地提高了人脸检测的帧处理速度,并降低了人脸检测的误检率。对基于Meanshift算法和基于光流法的人脸跟踪方法进行了研究与分析,分析了各算法的优缺点,利用各算法之间性能上的互补性,通过各模块之间的协同合作设计并实现了一个实时人脸检测与跟踪系统,并设定了相应的评价参数,通过实验室录制视频和NRC-IIT视频人脸数据库对系统进行了性能测试。     

一种多人脸跟踪算法的研究与实现

结合人脸的运动模型提出了一种多人脸的跟踪算法,通过使用跟踪稳定系数来解决多人脸跟踪过程中目标发生粘连重叠或者由于目标检测质量的原因造成跟踪目标丢失等各种问题;同时目标跟踪器提供一个预测搜索区域指导下一帧的定位,从而进一步提高定位的速度.实验结果表明此算法具有很好的准确性和实时性.     

银行营业场所智能视觉监控系统

介绍一种自动人脸检测与跟踪的智能视觉监控系统,它可以获取进入银行营业场所人员的清晰正面像.结合自适应阚值判定运动像素点分离运动目标、肤色特征、多级Adaboost分类器和Hausdorff距离实现实时人脸定位;用检测到的人脸位置来初始化跟踪算法实现快速人脸跟踪;以及基于对象的视频编码传输以及算法的DSP实现.     

多人脸跟踪与最佳人脸提取

针对视频人脸识别系统中同一人脸重复识别的问题,文中提出了一种多人脸跟踪与最佳人脸提取的方法。通过ViBe算法提取运动区域,缩小数据处理区域及确定执行人脸检测;利用Haar特征结合AdaBoost算法检测人脸,并根据肤色检测判断是否有误检;利用CamShift算法跟踪人脸;再使用Sobel算子得到清晰的人脸图片。实验表明,该方法下人脸误检率由2.8%降到0.2%,对于100帧视频平均处理时间从原始每帧112 ms降低到了45.6 ms,其处理速度明显提升。     

具有自适应MB-LBP前置滤波的CAMShift人脸跟踪算法

基于颜色分布的连续自适应均值移动（CAMShift）人脸跟踪算法简单、易于实现,被广泛应用于实时跟踪。但因其采用肤色模型作为跟踪模式,所以当目标处于类肤色背景区域时,跟踪窗口极易错误收敛到背景区域从而导致跟踪失败。为此,本文提出一种具有自适应LBP前置滤波的CAMShift跟踪算法。首先训练一个能检测人脸基本特征的级联MB-LBP节点分类器。当跟踪窗口进入类肤色干扰区时,系统自适应地把该分类器接入作为CAMShift跟踪算法的前置滤波器,以排除背景中的类肤色干扰,提高算法的鲁棒性。实验结果表明,该算法既能有效排除背景中的类肤色干扰、显著提高CAMShift人脸跟踪算法的鲁棒性,又能保持人脸跟踪的实时性。      

WSN中一种改进的目标跟踪方法

目标跟踪作为无线传感器网络(WSN)的一项基本应用,已得到广泛研究。提出了一种改进的目标跟踪方法,该方法主要分为邻域检测、目标跟踪和目标修正3个阶段。节点通过获取对于目标的感知信息收益值来实现邻域检测。每个节点通过计算自己的节点权值来决定是否参与目标的跟踪。基于目标的运动趋势,通过发送数据报告来自适应地对目标进行修正。仿真实验表明,该算法减少了参与目标跟踪的节点数,节省了能量,与PM算法相比,该算法提高了目标跟踪的准确率。     

人脸考勤系统中人脸视觉特征融合跟踪算法

基于人脸考勤系统提出了一种人脸视觉特征融合的跟踪算法,并将其扩展到多人脸跟踪场合.该算法充分利用了人脸的颜色信息和轮廓梯度信息,实现了两种特征的互补.最后,给出了人脸跟踪仿真结果.仿真结果表明,该人脸跟踪算法有效且稳健性较好,能够很好地处理人脸旋转、遮挡等问题,具有很好的应用前景.     

基于器官跟踪的人脸实时跟踪方法

头肩序列图像的人脸跟踪有着广泛的应用,但目前的大多数跟踪算法难以同时满足精确与快速的要求.本文提出了一种基于器官跟踪的人脸实时跟踪算法,利用形态学运算对嘴进行跟踪,进而通过对人脸对称性的分析,实现了对头肩序列中人脸的跟踪.由于算法只涉及简单的形态学运算^[1]和局部的旋转运算,速度达到实时要求,同时人脸对称性的分析保证了跟踪结果的精确性.     

Fast two-cycle curve evolution with narrow perception of background for object tracking and contour refinement

The problem of object contour tracking in image sequences remains challenging, especially those with cluttered backgrounds. In this paper, the fast two-cycle level set method with narrow perception of background (FTCNB) is proposed to extract the foreground objects, e.g. vehicles from road image sequences. The curve evolution of the level set method is implemented by computing the signs of region competition terms on two linked lists of contour pixels rather than by solving partial differential equations (PDEs). The curve evolution process mainly consists of two cycles: one cycle for contour pixel evolution and a second cycle for contour pixel smoothness. Based on the curve evolution process, we introduce two tracking stages for the FTCNB method. For coarse tracking stage, the speed function is defined by region competition term combining color and texture features. For contour refinement stage which requires higher tracking accuracy, the likelihood models of the Maximum a posterior (MAP) expressions are incorporated for the speed function. Both the tracking and refinement stages utilize the fast two-cycle curve evolution process with the narrow perception of background regions. With these definitions, we conduct extensive experiments and comparisons for the proposed method. The comparisons with other baseline methods well demonstrate the effectiveness of our work.     

Active contours for tracking distributions

A new approach to tracking using active contours is presented. The class of objects to be tracked is assumed to be characterized by a probability distribution over some variable, such as intensity, color, or texture. The goal of the algorithm is to find the region within the current image, such that the sample distribution of the interior of the region most closely matches the model distribution. Two separate criteria for matching distributions are examined, and the curve evolution equations are derived in each case. The flows are shown to perform well in experiments.     

基于显著性区域检测和水平集的图像快速分割算法

为了实现含有复杂背景和弱边界图像的快速准确分割,传统的水平集常采用重新初始化的方法,但是这种方法存在计算量大、分割不准确等问题。因此,结合显著性区域,该文提出一种基于边缘信息与区域局部信息结合的变水平集图像快速分割方法。首先用元胞自动机模型检测出图像的显著性区域,得到图像的初始化边界曲线。然后,采用改进的距离正规化水平集演化(Distance Regularized Level Set Evolution, DRLSE)模型把图像的局部信息结合到变分能量方程中,用改进的能量方程去指导曲线的演化。实验结果表明,与DRLSE模型相比,提出的算法平均消耗的时间只需要前者的2.76%,且具有较高的分割准确性。     

结合边界和区域的水平集超声图像分割算法

针对距离正则化的水平集演化（DRLSE）模型难以处理弱边缘图像、初始轮廓敏感以及曲线演化方向单一等问题,提出一种结合边缘和区域信息的变分水平集超声图像分割模型。该模型采用改进的四阶偏微分方程进行滤波,实现在去除噪声的同时保护图像边缘信息;构造了自适应加权系数,实现曲线自适应地向内或者向外演化;引入CV模型的外部能量项,将图像的边缘信息和区域信息相结合,提高了全局分割能力。实验结果表明：该方法在分割超声图像时,具有演化结果稳定,边缘定位准确的特点,可以较好地提取超声图像中的目标。     

A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations

In this paper, we develop a novel region-based approach to snakes designed to optimally separate the values of certain image statistics over a known number of region types. Multiple sets of contours deform according to a coupled set of curve evolution equations derived from a single global cost functional. The resulting active contour model, in contrast to many other edge and region based models, is fully global in that the evolution of each curve depends at all times upon every pixel in the image and is directly coupled to the evolution of every other curve regardless of their mutual proximity. As such evolving contours enjoy a very wide “field of view,” endowing the algorithm with a robustness to initial contour placement above and beyond the significant improvement exhibited by other region based snakes over earlier edge based snakes.     

Constraining active contour evolution via Lie Groups of transformation

We present a novel approach to constraining the evolution of active contours used in image analysis. The proposed approach constrains the final curve obtained at convergence of curve evolution to be related to the initial curve from which evolution begins through an element of a desired Lie group of plane transformations. Constraining curve evolution in such a way is important in numerous tracking applications where the contour being tracked in a certain frame is known to be related to the contour in the previous frame through a geometric transformation such as translation, rotation, or affine transformation, for example. It is also of importance in segmentation applications where the region to be segmented is known up to a geometric transformation. Our approach is based on suitably modifying the Euler-Lagrange descent equations by using the correspondence between Lie groups of plane actions and their Lie algebras of infinitesimal generators, and thereby ensures that curve evolution takes place on an orbit of the chosen transformation group while remaining a descent equation of the original functional. The main advantage of our approach is that it does not necessitate any knowledge of nor any modification to the original curve functional and is extremely straightforward to implement. Our approach therefore stands in sharp contrast to other approaches where the curve functional is modified by the addition of geometric penalty terms. We illustrate our algorithm on numerous real and synthetic examples.     

一种红外和可见光双通道视频目标跟踪方法

室外场景下由于场景背景条件变化容易导致视频目标跟踪稳定性差。该文提出一种利用红外和可见光传感器的双通道视频目标跟踪方法。该算法利用可见光图像的目标颜色特征和红外图像的目标轮廓特征,结合均值漂移算法与水平集曲线演化实现目标定位,并给出了目标尺度和模板更新方法;对多目标跟踪的互相遮挡问题,通过判断目标合并与分离实现遮挡时多个目标的定位。实验结果表明,该文方法能够有效处理光照变化、阴影、遮挡等情况,实现目标的稳定跟踪。     

基于水平集算法后向散射的运动目标轮廓提取

基于距离选通的激光主动成像系统在大气条件下对运动目标成像时,为了在更大范围内搜索跟踪目标,距离选通初始门宽通常在微秒量级,进而引起后向散射,严重影响了系统对目标的识别与提取。文中首先分析了基于距离选通的激光主动成像系统,在此基础上引入距离信息实时跟踪运动目标,研究了不同选通门宽引起的后向散射对图像质量的影响,提出了一种基于Sobel 算子和 GAC 模型的目标轮廓提取算法,将 Sobel 边缘检测的结果引入水平集边界停止函数,为水平集的曲线演化提供参考依据。实验结果表明,提出的水平集算法能够克服严重后向散射干扰影响提取目标轮廓,目标提取精度在0.9以上,高于 GAC、LIF、LBF 等经典水平集算法。     

基于改进脊线跟踪的手背血管识别算法研究

在脊线跟踪的基础上,直接在灰度图像上,沿纹线方向追踪血管脊线,在追踪的过程中,局部增强图像,局部设置域值,最后得到一幅血管脊线骨架图和附加其上的细节点信息.该方法有效减少了图像增强的计算量,避免了二值化在域值选取时对信息的丢失.相对于传统方法具有明显的速度优势,而且保持了较高的准确率.     

A real-time algorithm for the approximation of level-set-based curve evolution.

In this paper, we present a complete and practical algorithm for the approximation of level-set-based curve evolution suitable for real-time implementation. In particular, we propose a two-cycle algorithm to approximate level-set-based curve evolution without the need of solving partial differential equations (PDEs). Our algorithm is applicable to a broad class of evolution speeds that can be viewed as composed of a data-dependent term and a curve smoothness regularization term. We achieve curve evolution corresponding to such evolution speeds by separating the evolution process into two different cycles: one cycle for the data-dependent term and a second cycle for the smoothness regularization. The smoothing term is derived from a Gaussian filtering process. In both cycles, the evolution is realized through a simple element switching mechanism between two linked lists, that implicitly represents the curve using an integer valued level-set function. By careful construction, all the key evolution steps require only integer operations. A consequence is that we obtain significant computation speedups compared to exact PDE-based approaches while obtaining excellent agreement with these methods for problems of practical engineering interest. In particular, the resulting algorithm is fast enough for use in real-time video processing applications, which we demonstrate through several image segmentation and video tracking experiments.