Description: 小日本写的关于粒子滤波与人脸检测的源代码,能对人脸进行有效跟踪-small Japanese writing on the particle filter with face detection of source code, can face effective tracking Platform: |
Size: 2013 |
Author:bian_zg |
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Description: 小日本写的关于粒子滤波与人脸检测的源代码,能对人脸进行有效跟踪-small Japanese writing on the particle filter with face detection of source code, can face effective tracking Platform: |
Size: 2048 |
Author:bian_zg |
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Description: 清华大学学位论文;将树状多视角人脸检测器引入粒子滤波器的概率跟踪框架,提出了一种融合了检测信息的跟踪方法;对粒子滤波器的基本算法作扩展,提出了一种融合不同状态量的多个观测模型的方法-Degree Thesis, Tsinghua University the tree multiview face detection probability of the introduction of particle filter tracking framework, a combination of detection methods of tracking information of the basic particle filter algorithm, for the extension, a fusion different states than the volume of observation model Platform: |
Size: 1471488 |
Author:田卉 |
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Description: "Bayesian Mean Shift Face Tracker"
This is a paper about tracking by particle filter.
This is very useful. Platform: |
Size: 499712 |
Author:Wolf |
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Description: a real-time 3D pointing gesture
recognition algorithm for natural human-robot interaction
(HRI). The recognition errors in previous pointing gesture
recognition algorithms are mainly caused by the low performance
of the hands tracking module and by the unreliability
of the direction estimate itself, therefore our proposed algorithm
uses 3D particle filter for achieving reliability in hand
tracking and cascade hidden Markov model (HMM) for a
robust estimate for the pointing direction. When someone
enters the field of view of the camera, his or her face and
two hands are located and tracked using the particle filters.
The first stage HMM takes the hand position estimate and
maps it to a more accurate position by modeling the kinematic
characteristics of finger pointing. Platform: |
Size: 4096 |
Author:ALI |
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Description: real-time 3D pointing gesture
recognition algorithm for natural human-robot interaction
(HRI). The recognition errors in previous pointing gesture
recognition algorithms are mainly caused by the low performance
of the hands tracking module and by the unreliability
of the direction estimate itself, therefore our proposed algorithm
uses 3D particle filter for achieving reliability in hand
tracking and cascade hidden Markov model (HMM) for a
robust estimate for the pointing direction. When someone
enters the field of view of the camera, his or her face and
two hands are located and tracked using the particle filters.
The first stage HMM takes the hand position estimate and
maps it to a more accurate position by modeling the kinematic
characteristics of finger pointing. Platform: |
Size: 1064960 |
Author:ALI |
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Description: 这是一篇基于粒子波的红外人脸跟踪的文章,里面系统的提出了一种基于粒子滤
波的红外目标稳健跟踪新方法.-This is a particle-based face tracking infrared wave of articles, which the system presents a particle filter based tracking robust new method for infrared target. Platform: |
Size: 269312 |
Author:邢春 |
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Description: In this paper, we present a comparison of four novel algorithms
that have been applied to the tracking of people in
an indoor scenario. Tracking is carried out in 3D or 2D
(ground plane) to provide position information for a variety
of surveillance, HCI or meeting-support services. The
algorithms, based on background subtraction, face detection,
particle filter feature-matching and edge alignment
of a cylindrical model are described and comparative results
presented using independent test data produced and
ground-truthed for the EU CHIL project. Platform: |
Size: 235520 |
Author:Daria |
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Description: 了适应跟踪过程中目标光照条件的变化,并对目标特征进行在线更新,提出一种将局部二元模式(LBP)
特征与图像灰度信息相融合,同时结合增量线性判别分析对目标进行跟踪的算法.跟踪开始前,为了获得比较准确的目标描述,使用混合高斯模型和期望最大化算法对目标进行分割;跟踪过程中,通过蒙特卡罗方法对目标区域和背景区域进行采样,并更新特征空间参数.得到目标和背景的最优分类面;最后使用粒子滤波器结合最优分类面对目标状态进行预测.通过光照变化的仿真视频和自然场景视频的跟踪实验,验证了文中算法的有效性.-Tracking process to adapt to changes in the target lighting conditions, and the target feature for online updates, proposes a local binary pattern (LBP) features and image intensity information integration, combined with incremental linear discriminant analysis for target tracking algorithms. Track begins, in order to obtain a more accurate description of the objectives, the use of Gaussian mixture models and expectation maximization algorithm for target segmentation tracking process, through the Monte Carlo method of the target area and the background area sampled and updated feature space parameters. Get the optimal target and background classification surface finally Using Particle Filter optimal classification predict the state of the face of goal. By varying illumination simulation video and natural scenes video tracking experiment to verify the effectiveness of the proposed algorithm. Platform: |
Size: 608256 |
Author:wenping |
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Description: 小日本写的关于粒子滤波与人脸检测的源代码,能对人脸进行有效跟踪-small Japanese writing on the particle filter with face detection of source code, can face effective tracking Platform: |
Size: 2048 |
Author:thatth |
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Description: This thesis proposes a novel algorithm for integrated face detection and face tracking
based on the synthesis of an adaptive particle filtering algorithm and an AdaBoost face detection
algorithm. A novel Adaptive Particle Filter (APF), based on a new sampling technique, is
proposed to obtain accurate estimation of the proposal distribution and the posterior distribution
for accurate tracking in video sequences. The proposed scheme, termed a Boosted Adaptive
Particle Filter (BAPF), combines the APF with the AdaBoost algorithm. The AdaBoost
algorithm is used to detect faces in input image frames, while 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 confirm that the
proposed BAPF algorithm provides a means for robust face detection and accurate face tracking
under various tracking scenarios. Platform: |
Size: 1057792 |
Author:Trabelsi |
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Description: When processing a signal frequency analysis, Multi-target tracking particle filter, Face Recognition light treatment method. Platform: |
Size: 4096 |
Author:gieewhbn
|
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