基于运动分割和稀疏低秩分解的失真人体运动捕捉数据恢复

针对人体运动的复杂性和噪声干扰的无序性,提出一种基于运动分割和稀疏低秩分解的失真人体运动捕捉数据恢复方法.首先利用双边滤波对失真运动数据进行预修正,降低干扰数据的奇异信息并保证运动序列的连贯性;其次采用概率主元分析方法将修正后的运动数据进行语义行为自动分割,得到不同姿态的运动语义子区间;再利用加速近端梯度优化算法对每个失真运动子片段数据矩阵根据其更优低秩特性进行稀疏低秩分解,实现运动子片段数据的局部恢复;最后将局部恢复后的各子运动片段根据人体运动序列的时序特性组合,达到整体失真运动捕捉数据恢复的目的.实验结果表明,该方法能够有效地对失真人体运动数据进行恢复,效果显著,有助于重构逼近真实人体姿态的运动捕捉数据.     

Modeling Human Judgment of Digital Imagery for Multimedia Retrieval

The application of machine learning techniques to image and video search has been shown to boost the performance of multimedia retrieval systems, and promises to lead to more generalized semantic search approaches. In particular, the availability of large training collections allows model-driven search using a substantial number of semantic concepts. The training collections are obtained in a manual annotation process where human raters review images and assign predefined semantic concept labels. Besides being prone to human error, manual image annotation is biased by the view of the individual annotator because visual information almost always leaves room for ambiguity. Ideally, several independent judgments are obtained per image, and the inter-rater agreement is assessed. While disagreement between ratings bears valuable information on the annotation quality, it complicates the task of clearly classifying rated images based on multiple judgments. In the absence of a gold standard, evaluating multiple judgments and resolving disagreement between raters is not trivial. In this paper, we present an approach using latent structure analysis to solve this problem. We apply latent class modeling to the annotation data collected during the TRECVID 2005 Annotation Forum, and demonstrate how to use this statistic to clearly classify each image on the basis of varying numbers of ratings. We use latent class modeling to quantify the annotation quality and discuss the results in comparison with the well-known Kappa inter-rater agreement measure.     

人体运动仿真建模方法研究

为了研究人体在运动过程中的力学行为,探讨人体运动仿真的建模方法,运用多体系统动力学的建模方法建立了17刚体,55自由度的人体动力学模型,模型对人体颈部和下躯干的柔性效应给予了充分考虑,基于第一类Lagrange方程推导了系统的动力学方程,并以某人体为例,利用该模型对人体的行走过程进行了仿真计算,计算结果表明:采用所建立的人体动力学模型进行人体步态仿真、碰撞仿真等研究是现实可行的,最后结合对人体运动仿真的未来发展趋势的展望,说明人体动力学模型有待进一步完善.     

人体运动建模的实时逆运动学算法

人体运动的虚拟仿真及建模是当今计算机图形学研究的一个热点,而逆向运动学方法则是求解人体运动的一项关键技术.为了实现人体运动建模,提出一种新颖的基于人体关节约束的实时逆向运动学算法.首先基于骨骼长度改进了传统的循环坐标下降(CCD)算法,使其生成的运动效果更加流畅自然;然后引入生物分子运动模拟中的SHAKE算法,使短距离运动建模效果更加高效、鲁棒.在此基础上,提出了模拟多个节点同时运动的解析算法,以获得在多个关节点共同作用下的人体运动仿真效果.同运动捕捉的实测数据相比,采用文中算法所得的结果与在视觉效果上非常接近,并通过实验证实了其应用价值.     

基于概率PCA模型的三维人体运动分类

提出基于概率主成分分析的三维人体运动自动识别与分类算法.它根据不同类别的人体运动应各有自己代表性的特征集,采用概率PCA方法建立各类动作的高斯分布模型;然后构建基于最小错误率贝叶斯决策理论的多分类器,实现对未知的动作序列(或具有代表性的帧)进行多分类决策.该方法具有概率模型的优点,适合高维数据处理;同时,这种方法能够提取运动数据的内在特征,较好地消除了运动数据在时间轴上的差异带来的问题,从而准确地对运动数据进行分类,实验结果证明了本文方法的有效性.     

人体运动捕获数据的向量空间建模与检索

为了精确、高效地检索人体运动数据库,将三维人体运动捕获数据表示成类似于文本的形式,提出一种基于内容的运动检索方法.首先对人体上/下半身两部分数据分别提取关键帧,并进行相似传播聚类分析,获得数据中最具代表性的一组人体姿势,称之为运动词汇;然后将运动片段的每一帧都替换成运动词汇中与其最相近的姿势来构建运动文档,利用Bigr...     

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under‐constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables motion capture using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.     

捕捉与提取影像片断中的人物动作

该文详细分析了运用自适应最小二乘法捕捉和提取影像中人物动作行为的实验过程。并通过实验总结出线性预测,迭代调整,恢复丢失特征点等方法,并对自适应最小二乘法进行了改进。实验结果已经应用于新加坡南洋理工大学对影像中的个体动作进行动画复原的项目中,对于其它图像处理应用领域也有广阔的应用前景。     

Modeling Complex Unfoliaged Trees from a Sparse Set of Images

We present a novel image‐based technique for modeling complex unfoliaged trees. Existing tree modeling tools either require capturing a large number of views for dense 3D reconstruction or rely on user inputs and botanic rules to synthesize natural‐looking tree geometry. In this paper, we focus on faithfully recovering real instead of realistically‐looking tree geometry from a sparse set of images. Our solution directly integrates 2D/3D tree topology as shape priors into the modeling process. For each input view, we first estimate a 2D skeleton graph from its matte image and then find a 2D skeleton tree from the graph by imposing tree topology. We develop a simple but effective technique for computing the optimal 3D skeleton tree most consistent with the 2D skeletons. For each edge in the 3D skeleton tree, we further apply volumetric reconstruction to recover its corresponding curved branch. Finally, we use piecewise cylinders to approximate each branch from the volumetric results. We demonstrate our framework on a variety of trees to illustrate the robustness and usefulness of our technique.     

虚拟人几何建模及运动控制方法的研究

对目前虚拟人几何模型建市的几种常用方法进行了阐述,并对它们的优缺点进行了对比分析,同时对走步、跑步等几种典型的运动的控制方法进行了概述和对比研究.     

稀疏可交换图建模研究综述

可交换性假设是采用贝叶斯模型对网络数据建模的重要前提,基于Aldous-Hoover表示理论的可交换图不能生成稀疏网络.实证表明真实世界中的很多复杂网络都具有结点度幂律分布的稀疏特征,基于Kallenberg表示理论的可交换图能同时满足可交换性和稀疏性.本文以Caron-Fox模型和Graphex模型为例对稀疏可交换图建模的相关概念、理论和方法的研究发展进行了综述,首先讨论了随机图、贝叶斯非参数混合模型、可交换表示理论、Poisson点过程、离散非参数先验等理论的研究历程;然后介绍了Caron-Fox模型的表示;进而总结了进行稀疏可交换图的随机模拟所涉及的截断采样和边缘化采样方法;接下来综述了稀疏可交换图模型的后验推理技术.最后对稀疏可交换图建模的最新进展和研究前景做了介绍.     

Predicting Articulated Human Motion from Spatial Processes

We present a probabilistic interpretation of inverse kinematics and extend it to sequential data. The resulting model is used to estimate articulated human motion in visual data. The approach allows us to express the prior temporal models in spatial limb coordinates, which is in contrast to most recent work where prior models are derived in terms of joint angles. This approach has several advantages. First of all, it allows us to construct motion models in low dimensional spaces, which makes motion estimation more robust. Secondly, as many types of motion are easily expressed in spatial coordinates, the approach allows us to construct high quality application specific motion models with little effort. Thirdly, the state space is a real vector space, which allows us to use off-the-shelf stochastic processes as motion models, which is rarely possible when working with joint angles. Fourthly, we avoid the problem of accumulated variance, where noise in one joint affects all joints further down the kinematic chains. All this combined allows us to more easily construct high quality motion models. In the evaluation, we show that an activity independent version of our model is superior to the corresponding state-of-the-art model. We also give examples of activity dependent models that would be hard to phrase directly in terms of joint angles.     

单目视频中无标记的人体运动跟踪

提出一种人体运动跟踪算法,从无关节标记的单目视频中获取人体运动,利用一个带外观模板的人体关节模型,通过学习得到的运动模型及基于外观模型的相似性计算,巧妙地利用粒子滤波的概率密度传播策略鲁棒地跟踪普通单目视频中的人体运动,当出现跟踪丢失时,能在后续序列中自动恢复正确跟踪,且能较好地处理遮挡和自遮挡问题,实验表明,该算法鲁棒性好,跟踪结果令人满意。     

运动控制建模

机械、电气以及软件工程的集成正在改变运动控制设计及其发展的面貌。基于自己在传统机器制造业中的经验,Rockwell Automation的Kinetix Motion Control Business产品市场经理John Pritchard说,"单独的机械、控制和电气设计小组进行独立的工作,生产整台机     

Finding Syntactic Structures from Human Motion Data

We present a new approach to motion rearrangement that preserves the syntactic structures of an input motion automatically by learning a context‐free grammar from the motion data. For grammatical analysis, we reduce an input motion into a string of terminal symbols by segmenting the motion into a series of subsequences, and then associating a group of similar subsequences with the same symbol. To obtain the most repetitive and precise set of terminals, we search for an optimial segmentation such that a large number of subsequences can be clustered into groups with little error. Once the input motion has been encoded as a string, a grammar induction algorithm is employed to build up a context‐free grammar so that the grammar can reconstruct the original string accurately as well as generate novel strings sharing their syntactic structures with the original string. Given any new strings from the learned grammar, it is straightforward to synthesize motion sequences by replacing each terminal symbol with its associated motion segment, and stitching every motion segment sequentially. We demonstrate the usefulness and flexibility of our approach by learning grammars from a large diversity of human motions, and reproducing their syntactic structures in new motion sequences.     

复杂运动目标ISAR成像技术进展与展望

逆合成孔径雷达成像是宽带雷达实现目标信 息获取和精细描述的重要途径,但在成像过程中存在复杂运动导致散焦和成像分辨率不高两 个难题。针对目标复杂运动导致成像散焦问题,本文总结了微动对雷达波调制、进动目标成 像、 三维运动成像、分离部件成像等技术现状;针对成像分辨率不高问题,分析了多频段宽带回 波相参配准、融合成像等技术进展。本文介绍了雷达成像新技术,为解决上述两个难题带来 新的思路和途径,其中,雷达关联成像不依赖于雷达与目标的相对运动,可避免成像的复杂 运动补偿;太赫兹雷达成像容易实现大带宽和窄波束,获得目标精细雷达像。最后对新兴技 术的难点和发展趋势进行了展望。     

基于毫米波雷达稀疏点云的人体行为识别方法

目前利用毫米波雷达进行人体行为识别的方法在复杂场景下无法很好的区分相似动作,与此同时模型的鲁棒性和抗干扰能力也相对较差;针对以上两个问题,提出了一种通用的基于毫米波雷达稀疏点云的人体行为识别方法,该方法首先利用K-means++聚类算法对点云进行采样,然后使用基于注意力特征融合的点云活动分类网络进行人体行为特征的提取和识别,该网络可以兼顾点云的空间特征以及时序特征,对稀疏点云的运动有灵敏的感知能力;为了验证所提出方法的有效性和鲁棒性,分别在MMActivity数据集和MMGesture数据集上进行了实验,其在两个数据集上取得97.50%和94.10%的准确率,均优于其它方法;此外,进一步验证了K-means++点云采样方法的有效性,相较于随机采样,准确率提升了0.4个百分点,实验结果表明所提出方法能够有效的提升人体行为识别的准确率,且模型具有较好的泛化能力。     

Affine Approximation for Direct Batch Recovery of Euclidian Structure and Motion from Sparse Data

We present a batch method for recovering Euclidian camera motion from sparse image data. The main purpose of the algorithm is to recover the motion parameters using as much of the available information and as few computational steps as possible. The algorithm thus places itself in the gap between factorisation schemes, which make use of all available information in the initial recovery step, and sequential approaches which are able to handle sparseness in the image data. Euclidian camera matrices are approximated via the affine camera model, thus making the recovery direct in the sense that no intermediate projective reconstruction is made. Using a little known closure constraint, the F_A-closure, we are able to formulate the camera coefficients linearly in the entries of the affine fundamental matrices. The novelty of the presented work is twofold: Firstly the presented formulation allows for a particularly good conditioning of the estimation of the initial motion parameters but also for an unprecedented diversity in the choice of possible regularisation terms. Secondly, the new autocalibration scheme presented here is in practice guaranteed to yield a Least Squares Estimate of the calibration parameters. As a bi-product, the affine camera model is rehabilitated as a useful model for most cameras and scene configurations, e.g. wide angle lenses observing a scene at close range. Experiments on real and synthetic data demonstrate the ability to reconstruct scenes which are very problematic for previous structure from motion techniques due to local ambiguities and error accumulation.     

Viewpoint Selection for Human Actions

In many scenarios a dynamic scene is filmed by multiple video cameras located at different viewing positions. Visualizing such multi-view data on a single display raises an immediate question—which cameras capture better views of the scene? Typically, (e.g. in TV broadcasts) a human producer manually selects the best view. In this paper we wish to automate this process by evaluating the quality of a view, captured by every single camera. We regard human actions as three-dimensional shapes induced by their silhouettes in the space-time volume. The quality of a view is then evaluated based on features of the space-time shape, which correspond with limb visibility. Resting on these features, two view quality approaches are proposed. One is generic while the other can be trained to fit any preferred action recognition method. Our experiments show that the proposed view selection provide intuitive results which match common conventions. We further show that it improves action recognition results.