彩色序列图像中的人脸检测与跟踪*

提出了一种彩色视频序列图像中的人脸检测与跟踪方法.该方法将人脸检测与人脸跟踪有效地结合在一起,采用Condensation滤波跟踪算法对区域进行跟踪,在跟踪过程中提出引入基于支持向量机的人脸置信度,样本的置信度随时间进行更新,人脸检测的结果基于置信度的后验概率.同时,该方法对Condensation滤波跟踪算法作了改进,在跟踪过程中采用了基于Metropolis算法的重采样方法以及自适应的动态模型,实现了复杂背景下的对人脸自由运动的跟踪,且精度较高.实验结果表明,该方法有效地解决了复杂背景中人脸姿态变化情况下的人脸检测与跟踪问题,与静态人脸检测相比有更好的检测效果.     

基于肤色与肤色矩实时视频人脸检测与跟踪

提出了一种新颖、鲁棒、实时人脸检测与跟踪算法.该方法采用背景差分法提取运动区域,对运动区域利用肤色归一化RGB和HSV色彩模型的聚类性,得到人脸候选区域.利用人脸几何信息和孔洞信息对人脸候选区域进行验证.基于肤色矩特性,对人脸区域进行跟踪与预测.通过对不同背景条件下的人脸检测与跟踪,实验结果表明,所提算法不仅检测率高,且对光照,人脸姿态的变化具有较强的鲁棒性.基于480×360图像处理速度平均为25帧/秒,可满足系统实时性要求.     

在线修正的人脸姿态跟踪算法及交互应用

针对人脸在跟踪过程中可能存在大幅度的倾斜、旋转、遮挡以及肤色干扰等问题,提出一种基于在线修正的人脸跟踪算法.该算法当人脸检测失效时,人脸跟踪模块将用于提取目标参数;而在人脸跟踪过程中,为减小由连续跟踪造成的累积误差,利用人脸实时检测机制新检测到的人脸目标参数来修正跟踪模块的参数,包括跟踪窗口的位置和尺度,从而利用了人脸检测和人脸跟踪各自的优点.通过实验,其结果表明,该算法能够精确地跟踪复杂姿态下的人脸目标,并且能够解决肤色干扰和遮挡的问题,具有很好的适应性和鲁棒性.另外,将在线修正的跟踪方法应用于娱乐游戏控制,为人机交互提供了新的方式.     

条件主动外观模型下的人脸特征点跟踪

为了完成人脸关键特征点的精确定位跟踪,提出一种改进的基于反向合成匹配算法的条件主动外观模型匹配及其初始化算法.该算法假设已知正面人脸的关键特征点,首先通过建立散乱点对应与标定点对应之间的映射,根据给定的正面人脸标定点对任意姿态的侧面人脸进行自动初始标定,映射关系由核岭回归算法学习得到;将该标定点作为人脸跟踪算法的初始化点,然后利用条件主动外观模型反向合成匹配算法建立正面与任意姿态人脸的外观和形状模型,并对模型参数进行迭代优化;最后得到最优的任意姿态人脸的轮廓点,完成人脸跟踪.实验结果证明,与同类方法相比,该算法表现出了良好的性能,可在较短的计算时间内获得较高的定位精度.     

视频监控下的人脸跟踪与识别系统

针对智能视频监控系统的要求,设计了一个基于视频监控的自动多人脸跟踪识别系统,该系统的功能是实时跟踪视频监控范围内的人脸并鉴别人脸的身份。针对复杂背景及类似人脸区域的影响,提出了一种Adaboost人脸检测算法和主动形状模型相结合的人脸检测算法,实现人脸的准确检测;针对视频监控范围内人脸偏转、交错以及由于人员不断出入而导致人脸数目发生变化的问题,提出了CamShift和Kalman滤波器相结合的多人脸跟踪算法,同时对跟踪到的人脸进行实时身份识别。实验证明,该系统在视频监控范围内对人脸检测和身份识别准确,跟踪实时性好,是一种建立实时视频监控系统的实用方法。     

基于肤色模型和CAMShift算法的人脸检测与跟踪研究

讨论并实现一个基于肤色模型和CAMShift方法的人脸检测与跟踪原型系统。该系统采用肤色模型分割出视频帧中的肤色区域与非肤色区域以检测出人脸．利用CAMShift算法跟踪运动的人脸,完成对人脸各种姿态的跟踪,具有较好的实时性和鲁棒性。     

基于SURF特征点的多人脸跟踪方法研究

针对视频序列中多目标人脸跟踪问题,提出一种基于SURF(Speed-Up Robust Features)特征和KLT(Kanade-Lucas-Tomasi)匹配算法相结合的特征点跟踪方法。通过融合该方法,创新性地设计了一种多人脸跟踪系统框架,在目标出现明显的姿态、尺寸变化,或者遭遇局部遮挡、光照不充分等复杂环境干扰下,能够实现对目标人脸稳定跟踪。通过多组实验数据的对比,证明了该跟踪方法比Mean shift算法、传统KLT算法具有更好的鲁棒性,能获得更精确的运动信息;验证了多人脸跟踪系统能够在复杂环境下实现对多人脸的有效跟踪。     

基于肤色和AdaBoost算法的彩色人脸图像检测*

针对肤色检测对复杂背景下的图像误检率高和AdaBoost算法对多姿态、多人脸图像检测效果不理想的问题,将基于肤色的人脸检测与基于AdaBoost算法的人脸检测结合起来,提出一种新的人脸检测方法,即首先利用肤色和形态学操作分割肤色区域,再根据人脸区域的统计特性筛选出人脸候选区域,然后用AdaBoost级联分类器对候选区域扫描,以精确定位人脸.实验表明,该方法同时具有肤色检测正确率高与AdaBoost算法误检率低的优点,可以有效地运用于多姿态、多人脸和复杂背景的情况,具有较好的检测效果.     

基于人脸检测的人脸跟踪算法

文章提出了一种基于人脸检测技术的人脸跟踪算法。该算法利用前一帧的人脸检测结果预测当前帧中人脸可能的尺度与位置范围,在限定的范围内采用模板匹配与人工神经网分类的方法定位人脸,从而实现快速而可靠的人脸跟踪。由于使用了人脸检测技术,该方法可以自动定位初始人脸。实验表明该方法在具有复杂、动态变化背景的图象序列中是很有效的,速度为5－11Hz,可用于实时性系统。     

智能监控系统中自适应人脸检测跟踪算法改进

针对传统人脸检测跟踪算法在复杂环境中准确率不高,以及在跟踪过程中易受到周围相似色物体干扰、遮挡丢失等问题,提出了一种改进型自适应人脸检测跟踪算法.该算法通过人脸检测(Adaboost)与主动形状建模(ASM)算法相结合,降低了复杂环境中的人脸误检率;通过对运动目标跟踪(Camshift)算法提取H-S二维颜色概率直方图,并结合Kalman滤波器有效解决了相似色干扰及遮挡丢失问题.实验证明,改进型算法不仅在复杂环境中人脸检测率高、抗干扰能力强,且满足实时性的需求,是一种建立实时智能监控系统的实用方法.     

基于在线特征选择的实时多姿态人脸跟踪

This paper presents a system that is able to reliably track multiple faces under varying poses(tilted and rotated)in real time.The system consists of two interactive modules.The first module performs the detection of the face that is subject to rotation. The second module carries out online learning-based face tracking.A mechanism that switches between the two modules is embedded into the system to automatically decide the best strategy for reliable tracking.The mechanism enables a smooth transit between the detection and tracking modules when one of them gives either nil or unreliable results.Extensive experiments demonstrate that the system can reliably carry out real time tracking of multiple faces in a complex background under different conditions such as out-of-plane rotation,tilting,fast nonlinear motion,partial occlusion,large scale changes,and camera motion.Moreover,it runs at a high speed of 10～12 frames per second(fps)for an image of 320×240.     

A face detection and recognition system in color image series

A human face detection and recognition system for color image series is presented in this paper. The system is composed of two subsystems: human face detection subsystem and human face recognition subsystem. The face detection subsystem includes two modules: face finding and face verification. The human face finding module determines the face regions of a number of subjects from color image series using skin color analysis and motion analysis. The human face verification module is developed to verify the detected human faces by judging of eclipse and support vector machine (SVM), and precisely localize human faces by locating eyes and mouths based on Generalized Symmetry Transform. The features characterizing the relation between face patterns can be extracted and selected by Principal Component Analysis. Using these selected features to train multiple SVMs, we can finally classify human faces. Moreover, in these modules, several simple and complex methods are used to reduce the searching space. So the system can work at a high speed and high detection and recognition rate. Human face detection accuracy of the system is 97.2% under controllable lightning condition. Human face recognition accuracy of the system for 70 persons is 96.5% (with 20 eigenvectors) and 98.3% (with 30 eigenvectors).     

Development of head detection and tracking systems for visual surveillance

This paper proposes a technique for the detection of head nod and shake gestures based on eye tracking and head motion decision. The eye tracking step is divided into face detection and eye location. Here, we apply a motion segmentation algorithm that examines differences in moving people’s faces. This system utilizes a Hidden Markov Model-based head detection module that carries out complete detection in the input images, followed by the eye tracking module that refines the search based on a candidate list provided by the preprocessing module. The novelty of this paper is derived from differences in real-time input images, preprocessing to remove noises (morphological operators and so on), detecting edge lines and restoration, finding the face area, and cutting the head candidate. Moreover, we adopt a K-means algorithm for finding the head region. Real-time eye tracking extracts the location of eyes from the detected face region and is performed at close to a pair of eyes. After eye tracking, the coordinates of the detected eyes are transformed into a normalized vector of x-coordinate and y-coordinate. Head nod and shake detector uses three hidden Markov models (HMMs). HMM representation of the head detection can estimate the underlying HMM states from a sequence of face images. Head nod and shake can be detected by three HMMs that are adapted by a directional vector. The directional vector represents the direction of the head movement. The vector is HMMs for determining neutral as well as head nod and shake. These techniques are implemented on images, and notable success is notified.     

A multiobject tracking framework for interactive multimedia applications

Automatic initialization and tracking of multiple people and their body parts is one of the first steps in designing interactive multimedia applications. The key problems in this context are robust detection and tracking of people and their body parts in an unconstrained environment. This paper presents an integrated framework to address detection and tracking of multiple objects in a computationally efficient manner. In particular, a neural network-based face detector was employed to detect faces and compute person specific statistical model for skin color from the face regions. A probabilistic model was proposed to fuse the color and motion information to localize the moving body parts (hands). Multiple hypothesis tracking (MHT) algorithm was adopted to track face and hands. In real world scenes extracted features (face and hands) usually contain spurious measurements that create unconvincing trajectories and needless computations. To deal with this problem a path coherence function was incorporated along with MHT to reduce the number of hypotheses, which in turn reduces the computational cost and improves the structure of trajectories. The performance of the framework was validated using experiments on synthetic and real sequence of images.     

基于集散决策体系结构的智能车辆自主导航

智能车辆的体系结构作为智能车辆系统的基础,在构建智能车辆前必须得到合理的设计。为保证智能车辆系统的实时性和智能性,提出了基于集散决策的智能车辆体系结构。该结构由信息感知、规划决策、执行3个基本模块组成,其中规划决策分为低层次的分散决策和高层次的集中决策;分散决策对各种环境信息进行并行处理以得到各局部决策结果,集中决策对各分散决策结果进行综合判断并做出最终决策。按照以上设计思想,对道路环境下的智能车辆体系结构进行了仿真,同时实际构建了智能车辆车道识别及跟踪系统的体系结构。并进行了系统设计及实车试验。仿真结果表明,智能车辆能够根据实际环境信息做出合理决策,顺利完成车道跟踪、车距保持、换道行驶等任务。试验结果表明,在该体系结构控制下的智能车辆系统能够准确、可靠地完成车道识别、车道跟踪及车速保持任务。     

Tracking multiple people with recovery from partial and total occlusion

Robust tracking of multiple people in video sequences is a challenging task. In this paper, we present an algorithm for tracking faces of multiple people even in cases of total occlusion. Faces are detected first; then a model for each person is built. The models are handed over to the tracking module which is based on the mean shift algorithm, where each face is represented by the non-parametric distribution of the colors in the face region. The mean shift tracking algorithm is robust to partial occlusion and rotation, and is computationally efficient, but it does not deal with the problem of total occlusion. Our algorithm overcomes this problem by detecting the occlusion using an occlusion grid, and uses a non-parametric distribution of the color of the occluded person's cloth to distinguish that person after the occlusion ends. Our algorithm uses the speed and the trajectory of each occluded person to predict the locations that should be searched after occlusion ends. It integrates multiple features to handle tracking multiple people in cases of partial and total occlusion. Experiments on a large set of video clips demonstrate the robustness of the algorithm, and its capability to correctly track multiple people even when faces are temporarily occluded by other faces or by other objects in the scene.     

A novel visual interface for human-robot communication

The purpose of a robot is to execute tasks for people. People should be able to communicate with robots in a natural way. People naturally express themselves through body language using facial gestures and expressions. We have built a human-robot interface based on head gestures for use in robot applications. Our interface can track a person's facial features in real time (30 Hz video frame rate). No special illumination or facial makeup is needed to achieve robust tracking. We use dedicated vision hardware based on correlation image matching to implement the face tracking. Tracking using correlation matching suffers from the problems of changing shade and deformation or even disappearance of facial features. By using multiple Kalman filters we are able to overcome these problems. Our system can accurately predict and robustly track the positions of facial features despite disturbances and rapid movements of the head (including both translational and rotational motion). Since we can reliably track faces in real-time we are also able to recognize motion gestures of the face. Our system can recognize a large set of gestures (15) ranging from yes, no and may be to detecting winks, blinks and sleeping. We have used an approach that decomposes each gesture into a set of atomic actions, e.g. a nod for yes consists of an atomic up followed by a down motion. Our system can understand gestures by monitoring the transition between atomic actions.