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Wenlong Zheng Suchendra M. Bhandarkar 《Journal of Visual Communication and Image Representation》2009,20(1):9-27
A novel algorithm, termed a Boosted Adaptive Particle Filter (BAPF), for integrated face detection and face tracking is proposed. The proposed algorithm is based on the synthesis of an adaptive particle filtering algorithm and the AdaBoost face detection algorithm. An Adaptive Particle Filter (APF), based on a new sampling technique, is proposed. The APF is shown to yield more accurate estimates of the proposal distribution and the posterior distribution than the standard Particle Filter thus enabling more accurate tracking in video sequences. In the proposed BAPF algorithm, the AdaBoost algorithm is used to detect faces in input image frames, whereas the APF algorithm is designed to track faces in video sequences. The proposed BAPF algorithm is employed for face detection, face verification, and face tracking in video sequences. Experimental results show that the proposed BAPF algorithm provides a means for robust face detection and accurate face tracking under various tracking scenarios. 相似文献
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实现的人脸检测跟踪与特征点定位系统,基于VC++6.0开发平台,使用opencv作为开发工具,有效缩短了系统的开发时间。首先,本系统采用adaboost算法进行人脸检测,通过合理的特征模板的选择实现了人脸的实时检测;其次,人脸跟踪模块选用camshift算法,利用人脸检测模块生成的人脸坐标传递给跟踪模块,实现人脸的自动实时跟踪,同时建立多个camshift跟踪器对多人脸进行跟踪,并有效地解决了人脸遮挡的问题;最后,通过ASM(active shapemodel)算法实现了实时人脸特征点定位。实验结果表明该系统实现的人脸实时检测跟踪及特征点定位,效果明显,可以作为表情分析和情感计算、视频人脸识别开发的基础。 相似文献
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Significant appearance changes of objects under different orientations could cause loss of tracking, "drifting." In this paper, we present a collaborative tracking framework to robustly track faces under large pose and expression changes and to learn their appearance models online. The collaborative tracking framework probabilistically combines measurements from an offline-trained generic face model with measurements from online-learned specific face appearance models in a dynamic Bayesian network. In this framework, generic face models provide the knowledge of the whole face class, while specific face models provide information on individual faces being tracked. Their combination, therefore, provides robust measurements for multiview face tracking. We introduce a mixture of probabilistic principal component analysis (MPPCA) model to represent the appearance of a specific face under multiple views, and we also present an online EM algorithm to incrementally update the MPPCA model using tracking results. Experimental results demonstrate that the collaborative tracking and online learning methods can handle large pose changes and are robust to distractions from the background. 相似文献
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《Journal of Visual Communication and Image Representation》2008,19(6):382-391
A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and the face motion model and color model are created. The face motion model consists of a set of RVMs that learn the relationship between the motion of the face and its appearance, and the face color model is the 2D histogram of the face region in CrCb color space. In the tracking process different tracking methods (RVM tracking, local search, giving up tracking) are used according to different states of faces, and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces. In the full image search and local search, the similarity matrix is introduced to help matching faces efficiently. Experimental results demonstrate that this system can (a) automatically find new coming faces; (b) recover from occlusion, for example, if the faces are occluded by others and reappear or leave the scene and return; (c) run with a high computation efficiency, run at about 20 frames/s. 相似文献
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为了得到人眼跟踪过程中更好的鲁棒性和实时性以及跟踪精度,提出一种基于自适应增强分类算法(AdaBoost)、随机森林(RF)和时空上下文(STC)的重定位跟踪算法。该算法结构分为3层,分别为AdaBoost人脸检测、STC人脸跟踪和RF人眼定位。首先,利用AdaBoost在第一帧识别出人脸,从而提取出人脸窗口。接着,使用时空上下文跟踪算法进行人脸跟踪。然后,联合定向梯度直方图(HOG)算法进行相似度判断,以达到目标丢失后继续跟踪的目的。最后,采用随机森林算法进行人眼定位。实验结果表明,与传统的随机森林人眼跟踪算法相比,该算法在跟踪速度达到原方法的2倍左右,并在跟踪精度和鲁棒性上和原算法相同。基本满足在裸眼3D显示时人脸跟踪和人眼定位的精度高、实时性快、鲁棒性好的要求。 相似文献
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人脸检测是指检测并定位输入图像中所有的人脸,并返回精确的人脸位置和大小,是目标检测的重要方向。为了解决人脸尺度多样性给人脸检测造成的困难,该文提出一种新的基于单一神经网络的特征图融合多尺度人脸检测算法。该算法在不同大小的卷积层上预测人脸,实现实时多尺度人脸检测,并通过将浅层的特征图融合引入上下文信息提高小尺寸人脸检测精度。在数据集FDDB和WIDERFACE测试结果表明,所提方法达到了先进人脸检测的水平,并且该方法去掉了框推荐过程,因此检测速度更快。在WIDERFACE难、适中、简单3个子数据集上测试结果分别为87.9%, 93.2%, 93.4% MAP,检测速度为35 fps。所提算法与目前效果较好的极小人脸检测方法相比,在保证精度的同时提高了人脸检测速度。 相似文献
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自由立体显示技术中,人脸位置的探测与跟踪是关键之一.由于光照变化等因素的影响,对多人的脸部位置的探测很难达到快速、准确的目的.提出一种基于连续型Adaboost算法和Cascade结构的新方法.该方法采用红外主动照明模式,通过隔离可见光照,基本消除了光照变化对人脸检测造成的影响.新检测算法中Adaboost检测速度很快,Cascade结构可以检测那些难以识别的人脸,大大地提高了人脸检测的速度和鲁棒性.对视频流图像进行的检测实验中,没有出现人脸"漏检",极少出现非人脸的"误检".检测速度在Windows XP,Pentium IV,图片分辨率为640×480的条件下,可达25 f/s,完全达到了实时性的要求.另外,实验证明该方法对于人脸表情变化和人脸小角度倾斜也具有鲁棒性. 相似文献
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基于Boosting算法的实时人脸监控系统设计 总被引:1,自引:0,他引:1
设计实现了基于Boosting算法的实时人脸监控系统,以AdaBoost算法为人脸检测基础,以粒子滤波器算法为人脸跟踪基础,通过两者的结合提高了检测的速度.通过分析AdaBoost训练和检测的过程,指出影响AdaBoost检测速度的要素,并提出了通过区域生长等预处理方式对待检测图像进行区域合并,降低背景的复杂度,从而提高检测的速度;并增加了侧面人脸级联分类器,采用串并联结构将正面人脸和侧面人脸的检测综合起来,扩大了系统对人脸的检测范围.同时将跟踪结果作为人脸检测模块的反馈信号,增强了检测系统的目标捕获和目标校正能力. 相似文献
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《Signal Processing: Image Communication》2002,17(2):145-164
Automatic semantic video object extraction is an important step for providing content-based video coding, indexing and retrieval. However, it is very difficult to design a generic semantic video object extraction technique, which can provide variant semantic video objects by using the same function. Since the presence and absence of persons in an image sequence provide important clues about video content, automatic face detection and human being generation are very attractive for content-based video database applications. For this reason, we propose a novel face detection and semantic human object generation algorithm. The homogeneous image regions with accurate boundaries are first obtained by integrating the results of color edge detection and region growing procedures. The human faces are detected from these homogeneous image regions by using skin color segmentation and facial filters. These detected faces are then used as object seed for semantic human object generation. The correspondences of the detected faces and semantic human objects along time axis are further exploited by a contour-based temporal tracking procedure. 相似文献
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A real-time method is proposed to detect faces in videos. First it uses frame difference method to extract the motion area. Next the clustering character of skin is used to get the general face area. AdaBoost algorithm is applied to make concrete detection of human face. Finally an improved CamShift algorithm method is used to keep the tracking and improving algorithm speed. The experiments demonstrate the robustness and high speed of the proposed algorithm. 相似文献
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该文提出了一种基于几何主动轮廓模型的人脸跟踪方法.通过直方图反向投影,使人脸区域表现为一个一致性区域与背景相区别.研究了一种改进的窄带算法实现曲线演化:以等间隔分布的节点表示运动曲线,只在这些节点上计算Level set函数的变化值,窄带区内其余点的Level set值的更新通过插值和查表的方法实现;根据节点的局部图像信息决定节点的运动方向和时间步长值.实验表明该算法能在满足一定精度的前提下,快速地对运动人脸进行跟踪. 相似文献
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建立图象目标识别模型,用形状、灰度和运动特征描述图象目标。基于目标建模,把目标识别、门限及目标图形区域步级检测、虚漏警调节和目标空域条件有机地结合起来,给出牵引式跟踪系统中图象目标识别图形的自适应步级检测算法。该算法用于检测具有识别特征的图象目标图形,并成功地应用到实际的实时跟踪系统中的图象目标识别和跟踪。对实际图象的处理结果和在实时跟踪系统中的实验说明本文研究的技术的有效性和适用性。 相似文献
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This paper presents a method which utilizes color, local symmetry and geometry information of human face based on various models. The algorithm first detects most likely face regions or ROIs (Region-Of-Interest) from the image using face color model and face outline model, produces a face color similarity map. Then it performs local symmetry detection within these ROIs to obtain a local symmetry similarity map. The two maps and local similarity map are fused to obtain potential facial feature points. Finally similarity matching is performed to identify faces between the fusion map and face geometry model under affine transformation. The output results are the detected faces with confidence values. The experimental results demonstrate its validity and robustness to identify faces under certain variations. 相似文献
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Zhenqiu Zhang Gerasimos Potamianos Andrew W. Senior Thomas S. Huang 《Signal, Image and Video Processing》2007,1(2):163-178
The paper introduces a novel detection and tracking system that provides both frame-view and world-coordinate human location
information, based on video from multiple synchronized and calibrated cameras with overlapping fields of view. The system
is developed and evaluated for the specific scenario of a seminar lecturer presenting in front of an audience inside a “smart
room”, its aim being to track the lecturer’s head centroid in the three-dimensional (3D) space and also yield two-dimensional
(2D) face information in the available camera views. The proposed approach is primarily based on a statistical appearance
model of human faces by means of well-known AdaBoost-like face detectors, extended to address the head pose variation observed
in the smart room scenario of interest. The appearance module is complemented by two novel components and assisted by a simple
tracking drift detection mechanism. The first component of interest is the initialization module, which employs a spatio-temporal
dynamic programming approach with appropriate penalty functions to obtain optimal 3D location hypotheses. The second is an
adaptive subspace learning based 2D tracking scheme with a novel forgetting mechanism, introduced to reduce tracking drift
and increase robustness. System performance is benchmarked on an extensive database of realistic human interaction in the
lecture smart room scenario, collected as part of the European integrated project “CHIL”. The system consistently achieves
excellent tracking precision, with a 3D mean tracking error of less than 16 cm, and is demonstrated to outperform four alternative
tracking schemes. Furthermore, the proposed system performs relatively well in detecting frontal and near-frontal faces in
the available frame views.
This work was performed while Zhenqiu Zhang was on a summer internship with the Human Language Technology Department at the
IBM T.J. Watson Research Center. 相似文献
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Jian-Gang Wang Eng Thiam Lim Xiang Chen Ronda Venkateswarlu 《The Journal of VLSI Signal Processing》2007,49(3):409-423
Reported 3D face recognition techniques assume the use of active 3D measurement for 3D facial capture. However, active method
employ structured illumination (structure projection, phase shift, gray-code demodulation, etc) or laser scanning, which is
not desirable in many applications. A major problem of using passive stereo is its lower 3D face resolution and thus no passive
method for 3D face recognition has been reported. In this paper, a real-time passive stereo face recognition system is presented.
Entire face detection, tracking, pose estimation and face recognition are investigated. We used SRI Stereo engine that outputs
sub-pixel disparity automatically. An investigation is carried out in combining 3D and 2D information for face recognition.
The straightforward two-stage principal component analysis plus linear discriminant analysis is carried out in appearance
and depth face images respectively. A probe face is identified using sum of the weighted appearance and depth linear discriminant
distances. We investigate the complete range of linear combinations to reveal the interplay between these two paradigms. The
improvement of the face recognition rate using this combination is verified. The recognition rate by the combination is higher
than that of either appearance alone or depth alone. We then discuss the implementation of the algorithm on a stereo vision
system. A hybrid face and facial features detection/tracking approach is proposed which collects near-frontal views for face
recognition. Our face detection/tracking approach automatically initializes without user intervention and can be re-initialized
automatically if the tracking of the 3D face pose is lost. The experiments include two parts. Firstly, the performance of
the proposed algorithm is verified on XM2VTS database; Secondly, the algorithm is demonstrated on a real-time stereo vision
system. It is able to detect, track and recognize a person while walking toward a stereo camera.
相似文献
Jian-Gang WangEmail: |
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论文针对彩色图片的人脸检测在复杂的背景下检测难度大、检测时间长的问题,提出一种将非线性分段色彩变化的肤色模型、Gabor特征提取和多层感知机MLP分类决策相结合的人脸检测算法。该算法首先对输入图像进行自适应的光照补偿,根据非线性分段色彩变化建立的YCb'Cr'肤色模型筛选出潜在的人脸区域;然后对潜在人脸区域进行Gabor小波特征分析,利用MLP网络进行分类判别。通过计算机仿真得出此算法计算复杂度低、检测时间短。 相似文献