共查询到19条相似文献,搜索用时 93 毫秒
1.
2.
人体运动分析研究的若干新进展 总被引:6,自引:0,他引:6
人体运动视觉分析主要包括运动目标检测、运动目标分类、人体运动跟踪、人体行为识别与描述四个环节,在多领域具有广阔的应用前景.本文从上述四个方面综述了人体运动分析的研究现状,对人体运动分析的热点难点进行讨论,对可能的发展方向进行阐述和展望. 相似文献
3.
4.
针对传统人体动画制作成本高、人体运动受捕获设备限制等缺陷,提出了一种基于单目视频运动跟踪的三维人体动画方法。首先给出了系统实现框架,然后采用比例正交投影模型及人体骨架模型来恢复关节的三维坐标,关节的旋转欧拉角由逆运动学计算得到,最后采用H-anim标准对人体建模,由关节欧拉角驱动虚拟人产生三维人体动画。实验结果表明,该系统能够对人体运动进行准确的跟踪和三维重建,可应用于人体动画制作领域。 相似文献
5.
本文实现了一种基于计算机视觉技术的人体运动捕获方法,以简单的摄像头录制的单视图视频作为素材,从图像序列中提取并描述人体轮廓的运动,然后对其进行识别,捕获得到人体运动骨架模型,最后利用三维虚拟人进行三维仿真。 相似文献
6.
7.
视频中不完全运动特征的跟踪算法 总被引:4,自引:0,他引:4
在基于视频的人体运动捕获中,常用的特征跟踪算法对光照条件、图像噪音等非常敏感,而且不规则运动常使特征点或重叠、或自遮挡、或从视域中消失,给视频中运动特征跟踪带来很大的困难.为了有效地跟踪这些不完全运动的特征,提出一种有效的特征跟踪算法.实验结果表明,该方法不但能快速、精确地跟踪孤立特征,而且能有效地解决视频序列中存在较大噪音和不完全运动特征的跟踪问题. 相似文献
8.
运动捕捉技术是计算机视觉和人体运动分析领域的研究热点,在计算机动画等领域拥有广泛的应用前景。在总结基于视觉的人体运动捕捉技术进展的基础上,分析运动跟踪、捕捉方法及技术难点,提出一种新的从视频提取人体运动信息,重现人体运动轨迹的方法、流程及系统设计框架。 相似文献
9.
通过附在人身上的微型传感器对人体运动进行实时捕获.使用MEMS数字传感器和计算、通信设备搭建一种运动捕获系统,将导航和机器人机构学算法进行裁剪整合,权衡并且优化了计算量和精度,得出一种能用于人体运动捕获的算法并植入运动捕获系统.理想情况实验表明:系统的捕获误差为±2°,能迅速跟踪运动.系统的整体实验发现:系统捕获精度可以达到专业运动捕获的70%,对系统的误差进行了分析.本系统在保证运动捕获精度和速度的情况下,使用廉价设备,不需要高通量的数据计算,兼具轻便,对人体运动干扰小. 相似文献
10.
人体运动捕捉及运动控制的研究 总被引:7,自引:0,他引:7
在论述了几种方式的人体运动捕捉的基础上,介绍了光学运动捕捉的关键技术,包括摄像机标定、标记点跟踪和三维重建技术。无标记点的运动捕捉是新兴的技术,它将捕获的图像进行分割并分析,然后用多种约束算法进行三维重建。基于运动捕捉的人体运动控制相关文献很少,文中列举了现有各种应用运动捕捉数据的方式,包括基于关键帧、运动学和物理模型等的应用。论文对运动捕捉及运动控制技术进行了总结,可为此领域的研究提供有用的信息。 相似文献
11.
基于模型的人体运动跟踪 总被引:18,自引:0,他引:18
在计算机视觉领域,人体运动分析的的研究正因其广泛的应用前景而越来越受到研究 重视,对图像序列中的人体运动进行了跟踪是其中的关键技术。由于人体运动的特殊复杂性,已有的研究方法都对人体加上了许多限制条件。 相似文献
12.
Liang WangAuthor VitaeWeiming HuAuthor Vitae Tieniu TanAuthor Vitae 《Pattern recognition》2003,36(3):585-601
Visual analysis of human motion is currently one of the most active research topics in computer vision. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Human motion analysis concerns the detection, tracking and recognition of people, and more generally, the understanding of human behaviors, from image sequences involving humans. This paper provides a comprehensive survey of research on computer-vision-based human motion analysis. The emphasis is on three major issues involved in a general human motion analysis system, namely human detection, tracking and activity understanding. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are discussed. 相似文献
13.
14.
基于视频序列的人体运动分析系统的研究与实现 总被引:1,自引:1,他引:1
基于视频的人体运动分析是通过视频序列对人体的运动进行检测、分割和跟踪,获得人体或者某一部位的运动信息,生成人体的运动轨迹以及各运动学和动力学参数,达到分析人体三维运动的目的。人体运动属于连接型非刚体运动的范畴,具有高度的非线性特点,利用视频进行人体运动分析,有着极大的研究价值和现实应用前景。本文以单目视频中人体的跳远运动为研究对象.研究与实现了一个基于视频序列的人体运动分析系统。 相似文献
15.
运动协调是多移动机器人系统领域的主要研究热点之一。在阐述多机器人合作与运动协调两者关系的基础上,给出了多机器人系统运动协调的问题描述及其分类;从主要研究方法的角度,归纳总结了多机器人系统运动协调的国内外研究动态。最后,对运动协调在多移动机器人系统领域的前景和研究方向作出了展望。 相似文献
16.
鉴于非刚体的运动分析业已成为计算机视觉中的一个重要应用领域,为了使人们对该领域的研究现状有个概略了解,首先基于微分几何上的高斯曲率变化,对不同非刚性物体(简称非刚体)进行了分类,指出可把所有运动物体分为8类;然后对该领域目前存在的各种算法进行归纳总结,指出可把它们分为基于特征的方法和基于形状模型的方法两大类,并讨论了这两类方法各自的优势和不足之处;最后分析了该领域面临的困难,并展望了它未来可能的发展方向。同时指出,非刚性运动的视觉分析虽是一个蓬勃发展的研究领域,但目前仍处于初期阶段,因为近年来所进行的工作只是涉及众多困难问题中的较少部分,而且现有的各种模型和算法都还很不完善,人们还远未找到解决非刚性运动视觉分析问题的最有效的途径,但已有成果表明,一些相关研究领域,如语音识别和计算机图形学将会对该领域的发展提供帮助。 相似文献
17.
视频序列中人体运动目标的检测与跟踪研究 总被引:3,自引:0,他引:3
提出一种视频序列中人体运动目标的精确检测、提取以硬跟踪算法。该算法采用帧间差闽值法(简称TIFD)实现快速精确地检测和提取目标,使用扩展的Kalman滤波器预测运动目标下一时刻可能处于的区域,缩小了目标跟踪时的搜索范围。充分利用运行目标检测的结果,提高了目标的匹配效率及跟踪速度。同时给出了相应的实验结果,结果表明方法是比较实用的,能满足人体运动分析的基本要求。 相似文献
18.
The primary objective of this study was to investigate the intensity of perceived affect and emotion of a scene in relation to individual empathy ability and the rotation angle of a seat's motion caused by 4D movie motion effect. The secondary objective was to identify the causal factors that affected satisfaction with a motion effect. Although 4D movies have received increasing attention from the users of audiovisual content, user-centered research related to motion effects has rarely been conducted. Thirty-six participants who were grouped according to their empathy ability, viewed 10 4D movie clips and answered 2 questions related to 1) the perceived affect and emotion felt from the scenes and 2) satisfaction with the motion effects. The participants who had a high empathy ability indicated a stronger perceived affect and emotion during a scene than the participants who had a low empathy ability. A motion effect elicited different perceived emotions and affects between the two groups. The perceived temporal appropriateness, physical appropriateness, understandability, and disturbance of a motion effect affected the participants' satisfaction. This is a pioneering research related to the motion effects of 4D movie; therefore, these results could be helpful in providing insight for 4D effect designers of contents industry. 相似文献
19.
A new robust algorithm for motion detection and precise evaluation of the motion vectors of moving objects in a sequence of
images is presented. It is well known that the accuracy of estimating motion vectors estimation is limited by smoothness constraints
and mutual occlusions of motion segments. The proposed method is a fusion of block-matching motion estimation and global optimization
technique. It is robust to motion discontinuity and moving objects occlusions. To avoid some contradictions between global
optimization techniques and piece-wise smooth values of sought motion vectors, a hidden segmentation model is utilized. Computer
simulation and experimental results demonstrate an excellent performance of the method in terms of dynamic motion analysis.
This article was translated by the authors.
Mikhail Mozerov received his MS degree in Physics from the Moscow State University in 1982 and his PhD degree in Image Processing from the
Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. He works at the Laboratory of Digital
Optics of the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include
signal and image processing, pattern recognition, digital holography.
Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, and his PhD degree
in 1992 and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems,
Russian Academy of Sciences. Now he is a titular researcher at the Centro de Investigación Científica y de Educación Superior
de Ensenada (Cicese), México. His research interests include signal and image processing, pattern recognition.
Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received candidate’s degree in 1953 and doctoral
degree in information theory in 1972. At present, he is Emeritus Professor at the Institute of Information Transmission Problems
of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems.
He is a Member of IEEE, Popov Radio Society. 相似文献