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kalman for human motion tracking
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opencv实现的踪源码,简单易懂,opencv implementation of human motion tracking source, easy-to-read
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opencv实现的踪源码,简单易懂,opencv implementation of human motion tracking source, easy-to-read
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静态图像中的人体检测技术在驾驶员辅助系统、人体运动捕捉、色情图片过滤以及虚拟视频等领域有重要的应用价值。,Static images of the human body detection technology in driver assistance systems, human motion capture, filter pornography and other areas as well as the virtual video has important application va
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人脸视频图像编码是近年来图像编码领域里的一个研究热点问题,它在通信、互联网等方面有着广泛的应用前景。人脸图像编码的研究包含很多子问题,主要的三个方面为:精确的人脸目标定位算法,实时的人脸目标跟踪算法和高效的人脸图像编码方法。本文的主要研究工作在于: 1) 提出帧间差分和背景差分相结合的人脸目标定位算法,在人脸特征选择上,主要使用形状特征,通过椭圆拟合来得到人脸的位置。 2) 采用基于Kalman滤波的运动预测方法对人脸的区域变化进行预测,以此缩小目标可能存在的区域(ROI).-Facial vi
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human motion tracking discribtion
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人运动的视觉分析系统一般遵从下述的处理过程:1)运动检测;2)运动目标分类;3)人的跟踪;4)行为理解与描述-Visual human motion analysis system for the processing of general compliance with the following: 1) motion detection 2) moving target classification 3) human tracking 4) understand and describ
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Tracking people in a dense crowd is a challenging problem
for a single camera tracker due to occlusions and extensive
motion that make human segmentation difficult. In this
paper we suggest a method for simultaneously tracking all
people in a
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人体识别跟踪程序,可自动计算识别人体,,并根据运动趋势跟踪,重点设别头部和面部
-Human identification and tracking program can automatically calculate the recognition body, and tracking based on motion trend, focusing on device head and facial
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opencv实现的人体运动跟跟踪源码,简单易懂可直接使用。
-opencv realized human motion and tracking source, easy to understand can be used directly.
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人体跟踪源代码,有运动检测和跟踪结果。其中运动检测中有背景减减法,二值化,性态滤波,纵向投影,横向投影,除去影子,新纵投影
-The human tracking source code, motion detection and tracking results. Wherein the motion detection with background subtraction reduction, binarization, the sexual state filter, longitu
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在广泛研究前人工作的基础上,本文对运动人物的检测、跟踪与识别进行了综述。为使分类清楚明了,本文将人物运动分析分为两大类:人体运动分析,人脸分析。人体运动分析包括括人的较大幅度的肢体运动,如手势识别、步态分析、整个人体的运动分析;人脸分析包括人脸检测与识别、表情分析。在详细介绍了国内外研究现状后,提出了存在的问题及研究前景。
-On the basis of previous work in the extensive research, detection, tracking and rec
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Introduction: The goal of the HumanEva dataset is to develop new techniques, technology, and
algorithms for the automatic articulated pose estimation and tracking of humans. Brown University
is involved in an ongoing effort to develop this datase
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human-tracking : Human motion tracking graduation design code, the use of some of the opencv function
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