基于深度迁移学习的人体舞蹈姿态检测方法

常规的人体舞蹈姿态检测方法存在多姿态动作检测精度和识别率较低，因此提出基于深度迁移学习的人体舞蹈姿态检测方法。首先，利用Kinect传感器采集人体舞蹈姿态动作三维数据。其次，基于滑动窗口间接分割原理识别动作类别。再次，利用深度迁移学习建立动作识别模型，识别人体舞蹈的特定动作和非特定动作。最后，结合人体关节位置特征，检测人体舞蹈姿态动作中左手、右手、上身及全身4类局部特征信息。实验分析可知，新的方法应用后，舞蹈姿态检测的交并比值较高，显著提升了检测准确性。     

动作捕捉技术在舞蹈姿态分析和教学中的实践研究

为了将人体运动仿真技术应用到舞蹈姿态分析和教学研究中。研究了舞蹈姿态动作捕捉及分析,包括舞蹈动作的实时采集,舞蹈姿态的模型数据库建立,以及基于特征平面向量的21个关键节点和7个特征匹配平面的舞蹈姿态分析方法;然后,以某健美操班50名学员为实例,划分舞蹈基础和身体素质相当的两组分别进行常规教学和试验教学,试验对比组除了在动作幅度上低于常规组3.9分外,其在动作力度、规范程度方面的打分相比均高出9分以上。研究将动作捕捉技术与舞蹈教学和分析相结合,实现了舞蹈动作的直观反馈和分析,为科学的舞蹈教学提供了数据支撑。     

基于少量关键序列帧的人体姿态识别方法

针对传统人体姿态识别数据采集易受环境干扰、难以解决人体运动姿态的相似性和 人体运动执行者的特征差异性等问题,提出一种基于少量关键序列帧的人体姿态识别方法。首先 对原有运动序列进行预选,通过运动轨迹取极值的方法构造初选关键帧序列,再利用帧消减算法 获取最终关键帧序列;然后对不同人体姿态分别建立隐马尔科夫模型,利用 Baum-Welch 算法计 算得到初始概率矩阵、混淆矩阵、状态转移矩阵,获得训练后模型;最后输入待测数据,应用前 向算法,得到对于每个模型的概率,比较并选取最大概率对应的姿态作为识别结果。实验结果表 明,该方法能够有效的选取原始运动序列的关键帧,提高人体姿态识别的准确性。     

基于Hu不变矩特征优化的人体运动姿态识别算法

人体的运动过程较为复杂,图像中的相似动作很多,对传统的特征识别形成干扰,造成识别准确性不高。为了提高其识别正确率,提出一种Hu不变矩和人工鱼群优化支持向量机的人体运动姿态识别模型(Hu-AFSA-SVM)。首先,以二维连续图像为基础,提取图像中人体运动姿态识别的7个Hu不变矩,然后将其输入到SVM中进行训练,并采用AFSA对SVM参数进行优化,通过寻找一个最优超平面,尽可能在满足分类的限制条件下,将所有人体运动姿态分类数据集中的类别分开,在克服干扰下,完成识别。最后对其进行仿真实验。仿真结果表明,相对于其它识别模型,Hu-AFSA-SVM提高了人体运动姿态识别正确率,同时加快了识别速度,是一种有效的人体运动姿态识别方法。     

视频中多特征融合人体姿态跟踪

目的 目前已有的人体姿态跟踪算法的跟踪精度仍有待提高,特别是对灵活运动的手臂部位的跟踪。为提高人体姿态的跟踪精度,本文首次提出一种将视觉时空信息与深度学习网络相结合的人体姿态跟踪方法。方法 在人体姿态跟踪过程中,利用视频时间信息计算出人体目标区域的运动信息,使用运动信息对人体部位姿态模型在帧间传递;考虑到基于图像空间特征的方法对形态较为固定的人体部位如躯干和头部能够较好地检测,而对手臂的检测效果较差,构造并训练一种轻量级的深度学习网络,用于生成人体手臂部位的附加候选样本;利用深度学习网络生成手臂特征一致性概率图,与视频空间信息结合计算得到最优部位姿态,并将各部位重组为完整人体姿态跟踪结果。结果 使用两个具有挑战性的人体姿态跟踪数据集VideoPose2.0和YouTubePose对本文算法进行验证,得到的手臂关节点平均跟踪精度分别为81.4%和84.5%,与现有方法相比有明显提高;此外,通过在VideoPose2.0数据集上的实验,验证了本文提出的对下臂附加采样的算法和手臂特征一致性计算的算法能够有效提高人体姿态关节点的跟踪精度。结论 提出的结合时空信息与深度学习网络的人体姿态跟踪方法能够有效提高人体姿态跟踪的精度,特别是对灵活运动的人体姿态下臂关节点的跟踪精度有显著提高。     

基于姿态模型的人群目标分割方法

多相机间运动目标的跟踪与识别需要获得尽可能准确的目标区域。针对人群目标的粘连问题,提出一种基于姿态模型的人群目标分割方法。依据人体在运动过程中姿态的变化规律,构造7种出现频率较高的姿态模型。依次对单个目标和联合目标进行模型匹配,获得各个目标的位置、大小以及运动姿态信息。实验结果表明,该方法能有效解决相互遮挡情况下的目标分割问题。     

融合时序特征约束与联合优化的 点云3维人体姿态序列估计

目的 3维人体姿态估计传统方法通常采用单帧点云作为输入,可能会忽略人体运动平滑度的固有先验知识,导致产生抖动伪影。目前,获取2维人体姿态标注的真实图像数据集相对容易,而采集大规模的具有高质量3维人体姿态标注的真实图像数据集进行完全监督训练有一定难度。对此,本文提出了一种新的点云序列3维人体姿态估计方法。方法 首先从深度图像序列估计姿态相关点云,然后利用时序信息构建神经网络,对姿态相关点云序列的时空特征进行编码。选用弱监督深度学习,以利用大量的更容易获得的带2维人体姿态标注的数据集。最后采用多任务网络对人体姿态估计和人体运动预测进行联合训练,提高优化效果。结果 在两个数据集上对本文算法进行评估。在ITOP(invariant-top view dataset)数据集上,本文方法的平均精度均值(mean average precision,mAP)比对比方法分别高0.99%、13.18%和17.96%。在NTU-RGBD数据集上,本文方法的mAP值比最先进的WSM(weakly supervised adversarial learning methods)方法高7.03%。同时,在ITOP数据集上对模型进行消融实验,验证了算法各个不同组成部分的有效性。与单任务模型训练相比,多任务网络联合进行人体姿态估计和运动预测的mAP可以提高2%以上。结论 本文提出的点云序列3维人体姿态估计方法能充分利用人体运动连续性的先验知识,获得更平滑的人体姿态估计结果,在ITOP和NTU-RGBD数据集上都能获得很好的效果。采用多任务网络联合优化策略,人体姿态估计和运动预测两个任务联合优化求解,有互相促进的作用。     

基于惯性动作捕捉的人体运动姿态模拟

针对人体运动姿态编辑的自由性,提出一种人体运动姿态模拟方法。该方法采用贝塞尔曲线和数值数据编辑人体运动姿态。根据人体运动的特点,在VC++中运用OpenGL构建虚拟人体模型,利用动作捕捉技术设计人体运动姿态的模拟程序。结合人体关节正常活动范围,对主要关节点的运动姿态进行分析,结果表明,该方法能有效利用人体运动数据,驱动虚拟人体模型。     

基于多传感器数据融合的人体运动模式识别研究

针对单一传感器在人体运动姿态监测中误差较大的问题,提出了一种基于加速度传感器和陀螺仪数据融合的人体运动模式识别方法;该方法使用陀螺仪输出的人体运动信息对加速度传感器采集到的姿态角信息进行修正,采用卡尔曼滤波算法实现多传感器信息的融合,有效提高了姿态角度测量的准确度;根据人体日常的活动状态构建了基于人体姿态角度特征的隐马尔可夫模型实现人体运动模式的识别;实验表明,该方法比采用单一传感器方法识别的准确率高,可以有效区分不同的日常活动行为。     

融合时空图卷积网络与非自回归模型的三维人体运动预测

当前人体运动预测的方法大多采用基于图卷积网络的自回归模型,没有充分考虑关节间的特有关系和自回归网络性能的限制,从而产生平均姿态和误差累积等问题。为解决以上问题,提出融合时空图卷积网络和非自回归的模型对人体运动进行预测。一方面利用时空图卷积的网络提取人体运动序列的局部特征,可以有效减少三维人体运动预测场景中的平均姿态问题和过度堆叠图卷积层引起的过平滑问题的发生;另一方面将非自回归模型与时空图卷积网络进行结合,减少误差累计问题的发生。利用Human3.6M的数据集进行80 ms、160 ms、320 ms和400 ms的人体运动预测实验。结果表明,NAS-GCN模型与现有方法相比,能预测出更精确的结果。     

Target detection algorithm for dance moving images based on sensor and motion capture data

The effectiveness of classification based on motion capture data to identify the human skeleton dance types. The goal is based on the body joint's information is to perform the characteristic posture obtained by the ultra-high Kinect sensor, identifying for each dance. The proposed Target Detection (TD) algorithm is used to dance moving object identification based on the improved Gaussian Mixture Model (GMM). The proposed Target Detection (TD) algorithm based on a Gaussian Mixture Model is a widely used method for modeling background from a Kinect sensor moving objects. The used data set contains six folk dance sequences and their variations. Gesture recognition scheme using a plurality of time constraints, spatial information, and spatial distribution characteristics to create a training data set appropriate application. The Gaussian Mixed Model distribution background model account, the algorithm, and their frame difference can be extracted in straight lines to obtain target dance areas with less background and background photo station under motor damage conditions. Through real-time Target Detection (TD) algorithm dance moving images based on the Gaussian Mixture Model (GMM), these two algorithms effectively detect dance moving image targets.     

Fun and Accurate Static Balance Training to Enhance Fall Prevention Ability of Aged Adults: A Preliminary Study

Fall prevention is critical for aged adults as falling down has been cited as the number one cause of accidental death in aged adults. Static balance training is an effective method of increasing the balancing capacity in aged adults and reducing their fall risk. This article discusses a method that combines virtual reality (VR) technology with motion‐capture devices for aged adults to practice static balance training in a fun and accurate environment. The aged adults wore motion‐capture devices to interactively manipulate a three‐dimensional (3D) human model (HM) in a virtual environment to follow a 3D posture model (PM). The proposed method was implemented using commercial, off‐the‐shelf 3D tools. A preliminary usability study was conducted, and the results show that the parameters of PM design will affect the aged adult's posture control performance. Our method has several advantages over the currently existing approaches. First, by adjusting the PM parameters, different posture control programs could be designed to support customized static balance training. Second, collision detections between the HM and PM could be used to accurately monitor the posture control process and to interactively provide feedback to the trainee. Third, posture control quality could be quantitatively evaluated by analyzing the collision data between the HM and PM. The proposed method could be used to develop a fun, accurate, and customized static balancing capacity training environment for aged adults. © 2012 Wiley Periodicals, Inc.     

基于虚拟人设计与实现机器人舞蹈动作的一种方法

基于三维虚拟人的骨骼模型仿真实现了机器人的舞蹈动作。分析了机器人舞蹈动作运动的基本规律和实现动作的基本方法,基于此建立了三维虚拟人实现机器人舞蹈动作的关键帧数据库,在此基础上利用中间帧的过渡函数实现了虚拟人骨骼模型仿真机器人舞蹈动作的方法。实验中利用NOBODY机器人舞蹈视频实现了三维虚拟人骨骼模型的仿机器人的舞蹈动画,结果证明了方法的正确性和可行性,对于机器人的三维虚拟化和仿真研究具有重要意义。     

Example-based automatic music-driven conventional dance motion synthesis

We introduce a novel method for synthesizing dance motions that follow the emotions and contents of a piece of music. Our method employs a learning-based approach to model the music to motion mapping relationship embodied in example dance motions along with those motions' accompanying background music. A key step in our method is to train a music to motion matching quality rating function through learning the music to motion mapping relationship exhibited in synchronized music and dance motion data, which were captured from professional human dance performance. To generate an optimal sequence of dance motion segments to match with a piece of music, we introduce a constraint-based dynamic programming procedure. This procedure considers both music to motion matching quality and visual smoothness of a resultant dance motion sequence. We also introduce a two-way evaluation strategy, coupled with a GPU-based implementation, through which we can execute the dynamic programming process in parallel, resulting in significant speedup. To evaluate the effectiveness of our method, we quantitatively compare the dance motions synthesized by our method with motion synthesis results by several peer methods using the motions captured from professional human dancers' performance as the gold standard. We also conducted several medium-scale user studies to explore how perceptually our dance motion synthesis method can outperform existing methods in synthesizing dance motions to match with a piece of music. These user studies produced very positive results on our music-driven dance motion synthesis experiments for several Asian dance genres, confirming the advantages of our method.     

A method for expressing human posture as 3DCG using thermal image processing and 3D model fitting

An imitation of human motion has been used as a promising technique for the development of a robot. Some techniques such as motion capture systems and data-gloves are used for analyzing human motion. However, since those methods involve (a) environmental restrictions such as the preparation of two or more cameras and the strict control of brightness, and (b) physical restrictions such as the wearing of markers and/or data-gloves, they are far removed from a method for recognizing human motion in a natural condition. In this article, we propose a method that makes a 3-dimensional CG (3DCG) by transforming a feature vector of human posture on a thermal image into a 3DCG model. The 3DCG models for use as training data are made with manual model fitting. Then human models synthesized by our method are geometrically evaluated in CG space. The average error in position is about 10 cm. Such a relatively small error might be acceptable in some cases e.g., 3DCG animation generation and the imitation of human motion by a robot. Our method has neither physical nor environmental restrictions. The rotation-angles at each joint obtained by our method can be used for an imitation of human posture by a robot.     

Emotion Analysis and Classification: Understanding the Performers' Emotions Using the LMA Entities

The increasing availability of large motion databases, in addition to advancements in motion synthesis, has made motion indexing and classification essential for better motion composition. However, in order to achieve good connectivity in motion graphs, it is important to understand human behaviour; human movement though is complex and difficult to completely describe. In this paper, we investigate the similarities between various emotional states with regards to the arousal and valence of the Russell's circumplex model. We use a variety of features that encode, in addition to the raw geometry, stylistic characteristics of motion based on Laban Movement Analysis (LMA). Motion capture data from acted dance performances were used for training and classification purposes. The experimental results show that the proposed features can partially extract the LMA components, providing a representative space for indexing and classification of dance movements with regards to the emotion. This work contributes to the understanding of human behaviour and actions, providing insights on how people express emotional states using their body, while the proposed features can be used as complement to the standard motion similarity, synthesis and classification methods.     

基于视觉图像的舞姿自动空间分割系统设计

为了提高舞姿的视觉指导效果,需要进行舞姿的自动空间规划设计,提出一种基于视觉图像的舞姿自动空间分割方法。构建舞姿自动空间规划的视觉图像采集模型,采用高分辨的视觉信息增强技术进行舞姿自动空间视觉图像的信息增强处理,提取舞姿自动空间视觉特征量,采用三维视觉重构技术进行舞姿自动空间规划设计,构建舞姿自动空间规划的视觉图像多层网格区域分布模型,结合联合稀疏结构特征分解方法,进行舞姿视觉图像的融合和自适应分割处理,根据特征分割结果进行舞姿的多层空间结构规划和融合聚类,实现舞姿的自动空间分割的算法优化设计。在多层B/S构架体系下实现舞姿自动空间分割系统的软件开发设计。仿真结果表明,采用该方法能有效实现视觉图像下的舞姿自动空间分割,输出峰值信噪比较高,具有较强的舞姿指导性。     

基于动捕数据驱动的三维民族舞蹈展示方法

三维模型动画在数字化设计和应用中具有重要意义,受到越来越多研究者关注;但如何通过三维数字化原真再现民族舞蹈表演是极具挑战的问题.本论文通过动捕技术采集舞蹈动作实现舞蹈数字化展示.具体方法是:首先利用动捕设备捕获人体动作数据,然后在Maya中进行人物建模、骨骼绑定、蒙皮和权重调节,再通过MotionBuilder将3D模型与动捕数据结合,最终完成了现实舞蹈动作的虚拟人展演.论文构建了一个面向民族舞蹈展演的虚拟场景,并以13个民族的舞蹈为数字化内容,推广动捕驱动的舞蹈展演方法的应用.     

Calculation and visualization of the dynamic ability of the human body

There is a great demand for data on the mobility and strength capability of the human body in many areas, such as ergonomics, medical engineering, biomechanical engineering, computer graphics (CG) and virtual reality (VR). This paper proposes a new method that enables the calculation of the maximal force exertable and acceleration performable by a human body during arbitrary motion. A musculoskeletal model of the legs is used for the calculation. Using our algorithm, it is possible to evaluate whether a given posture or motion is a feasible one. A tool to visualize the calculated maximal feasibility of each posture is developed. The obtained results can be used as criteria of manipulability or strength capability of the human body, important in ergonomics and human animation. Since our model is muscle‐based, it is possible to simulate and visualize biomechanical effects such as fatigue and muscle training. The solution is based on linear programming and the results can be obtained in real time. Copyright © 1999 John Wiley & Sons, Ltd.