Review of techniques for motion capture data processing

In order to high reality and efficiency,the technique of motion capture (MoCap) has been widely used in the field of computer animation.With the development of motion capture,a large amount of motion capture databases are available and this is significant for the reuse of motion data.But due to the high degree of freedoms and high capture frequency,the dimension of the motion capture data is usually very high and this will lead to a low efficiency in data processing.So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can’t ignore.In this paper,first we lay out some problems about the key techniques in motion capture data processing.Then the existing approaches are analyzed and summarized.At last,some future work is proposed.     

一种新的光学运动捕捉数据处理方法*

提出一种适用于被动式光学人体运动捕捉散乱数据处理方法。该方法基于光学人体运动捕捉散乱数据的全局信息,提出基于模块分段线性模型的数据处理算法。利用模块分段线性模型归纳出不同模块的变化特征,从而确定各模块数据的匹配优先级及段内拟合函数,有效地对三维运动数据各模块进行全局性分层次预测和跟踪,并对噪声数据进行基于模块的去噪处理;对缺失运动数据提出基于分段Newton插值拟合算法,进行合理的补缺。该方法经优化后在处理过程中无须人工干预,并能满足实时性要求。     

A group of novel approaches and a toolkit for motion capture data reusing

Now more and more motion capture (MoCap) systems are used to acquire realistic and highly detailed motion data which are widely used for producing animations of human-like characters in a variety of applications, such as simulations, video games and animation films. And recently large MoCap databases are available. As a kind of emerging multimedia data, 3D human motion has its own specific data form and standard format. But to the best of our knowledge, only a few approaches have been explored for 3D MoCap data feature representation and reusing. This paper proposes a group of novel approaches for posture feature representation, motion sequence segmentation, key-frame extraction and content-based motion retrieval, which are all very important for MoCap data reusing and benefit to the efficient animation production. To validate these approaches, we set up a MoCap database and implemented a prototype toolkit. The experiments show that the proposed algorithms could achieve the approvable results.     

Multiscale motion saliency for keyframe extraction from motion capture sequences

Motion capture is an increasingly popular animation technique; however data acquired by motion capture can become substantial. This makes it difficult to use motion capture data in a number of applications, such as motion editing, motion understanding, automatic motion summarization, motion thumbnail generation, or motion database search and retrieval. To overcome this limitation, we propose an automatic approach to extract keyframes from a motion capture sequence. We treat the input sequence as motion curves, and obtain the most salient parts of these curves using a new proposed metric, called 'motion saliency'. We select the curves to be analysed by a dimension reduction technique, Principal Component Analysis (PCA). We then apply frame reduction techniques to extract the most important frames as keyframes of the motion. With this approach, around 8% of the frames are selected to be keyframes for motion capture sequences. Copyright © 2010 John Wiley & Sons, Ltd.     

Multiresolution coding of motion capture data for real-time multimedia applications

Multimedia Tools and Applications - In this work, we present a novel and efficient method for coding of motion capture (MoCap) data obtained from recording of human actions. MoCap data is...     

GDBC: A tool for generating global-scale distributed basin morphometry

In response to the vital role of geomorphological analysis in natural hazards study, geomorphology, distributed hydrology and other related disciplines, we present the first global basin morphometric product of 30 characteristics, 9 archived elementary including stream order, stream number, stream length, basin relief, basin length, basin perimeter, maximal flow length, down valley length and overland flow length, and 21 derivable from these elementary morphometric characteristics. Characteristics of basins discharging to every grid-cell of the global earth surface are generated at 30 arcsec resolution from the remotely sensed HydroSHEDS dataset. This product does not exist in previous literature because tree-structural grid-cells bring large computational redundancy to basin delineation and thus computing basin characteristics. To generate this product within a reasonable timeframe, we introduce in this paper an efficient framework to reduce the algorithm complexity to linear, O(N). We have presented a few geomorphologic findings using the generated product.     

A recognition-based motion capture baseline on the HumanEva II test data

The advent of the HumanEva standardized motion capture data sets has enabled quantitative evaluation of motion capture algorithms on comparable terms. This paper measures the performance of an existing monocular recognition-based pose recovery algorithm on select HumanEva data, including all the HumanEva II clips. The method uses a physically motivated Markov process to connect adjacent frames and achieve a 3D relative mean error of 8.9 cm per joint. It further investigates factors contributing to the error and finds that research into better pose retrieval methods offers promise for improvement of this technique and those related to it. Finally, it investigates the effects of local search optimization with the same recognition-based algorithm and finds no significant deterioration in the results, indicating that processing speed can be largely independent of the size of the recognition library for this approach.     

MATTER: A tool for generating end-to-end IoT test scripts

Software Quality Journal - In the last few years, Internet of Things (IoT) systems have drastically increased their relevance in many fundamental sectors. For this reason, assuring their quality is...     

人体运动实时捕捉设备传输数据的滤波与处理

简介人体运动实时捕捉设备的工作原理,对设备探测并传输来的数据进行特性分析,根据分析结论,提出了干扰数据识别与修复、纵向平滑滤波、首帧收敛化处理和值域限定等滤波和处理方法,并详细阐述了这些方法的综合运用。系统使用这些方法,经运行调试后,传输数据稳定,系统对Marker点的探测精度也符合系统设计的要求（≤6mm）,为实现各点坐标计算提供了必要条件。     

基于运动捕获数据的虚拟人动画研究

随着三维游戏等行业对计算机动画制作需求的增加,在三维动画制作软件中人工调整虚拟人动作的工作方式已经不再适合现在的计算机动画制作。运动捕获技术是直接记录物体的运动数据并将其用于生成计算机动画,具有高效率、所生成的动画真实感强等优点,因而获得了广泛应用。提出了一种利用运动捕获数据来生成动画的方法,基于运动捕获得到的数据建立并驱动三维骨架模型,从而产生骨架的运动,形成动画。该方法可以充分利用现有的大量运动捕获数据,因此具有较大的应用前景。     

基于PGA的人体运动捕获数据分割方法

由于人体运动捕获数据的固有非线性,线性方法并不总是能够有效地找到运动捕获数据的内在维度,针对这种情况,提出了基于主测地线分析（PGA）和概率主测地线分析（PPGA）的自动分割方法。这两种方法都将人体运动视为一个有序的姿势序列,并在姿势序列有局部变化处对运动进行分割。基于PGA的分割方法是在运动局部模型的内在维度突然增长处分配一个分割点,基于PPGA的分割方法是在姿势分布发生改变时放置分割点。实验结果表明,该方法都能实现自动分割,且具有较好的分割结果。     

RADCAP: A potential model tool for direct capture reactions

A computer program is presented aiming at the calculation of bound and continuum states, reduced transition probabilities, phase-shifts, photo-disintegration cross sections, radiative capture cross sections, and astrophysical S-factors, for a two-body nuclear system. The code is based on a potential model of a Woods-Saxon, a Gaussian, or a M3Y, type. It can be used to calculate nuclear reaction rates in numerous astrophysical scenarios.

Program summary

Title of program: RADCAP (RADiative CApture)Catalogue identifier:ADSHProgram summary URL:http://cpc.cs.qub.ac.uk/summaries/ADSHProgram obtainable from: CPC Program Library, Queen's University of Belfast, N. IrelandComputers: The code has been created on an IBM-PC, but also runs on UNIX machinesOperating systems: WINDOWS or UNIXProgram language used: Fortran-77Memory required to execute with typical data: 8 Mbytes of RAM memory and 2 MB of hard disk spaceNo. of bits in a word: 32 or 64Memory required for test run with typical data: 2 MBNo. of bytes in distributed program: 376 817No. of lines in distributed program, including test data, etc.: 3054Distribution format: tar gzip fileKeywords: Potential model, photodissociation, radiative capture, astrophysical S-factorsNature of physical problem: The program calculates bound and continuum wavefunctions, phase-shifts and resonance widths, astrophysical S-factors, and other quantities of interest for direct capture reactions.Method of solution: Solves the radial Schrödinger equation for bound and for continuum states. First the eigenenergy is estimated by using the WKB method. Then, a Numerov integration is used outwardly and inwardly and a matching at the nuclear surface is done to obtain the energy and the bound state wavefunction with good accuracy. The continuum states are obtained by a Runge-Kutta integration, matching the Coulomb wavefunctions at large distances outside the range of the nuclear potential.Typical running time: Almost all the CPU time is consumed by the solution of the radial Schrödinger equation. It is about 1 min on a 1 GHz Intel P4-processor machine for a Woods-Saxon potential.     

Searching for variable-speed motions in long sequences of motion capture data

Motion capture data digitally represent human movements by sequences of body configurations in time. Subsequence searching in long sequences of such spatio-temporal data is difficult as query-relevant motions can vary in execution speeds and styles and can occur anywhere in a very long data sequence. To deal with these problems, we employ a fast and effective similarity measure that is elastic. The property of elasticity enables matching of two overlapping but slightly misaligned subsequences with a high confidence. Based on the elasticity, the long data sequence is partitioned into overlapping segments that are organized in multiple levels. The number of levels and sizes of overlaps are optimized to generate a modest number of segments while being able to trace an arbitrary query. In a retrieval phase, a query is always represented as a single segment and fast matched against segments within a relevant level without any costly post-processing. Moreover, visiting adjacent levels makes possible subsequence searching of time-warped (i.e., faster or slower executed) queries. To efficiently search on a large scale, segment features can be binarized and segmentation levels independently indexed. We experimentally demonstrate effectiveness and efficiency of the proposed approach for subsequence searching on a real-life dataset.     

RMoCap: an R language package for processing and kinematic analyzing motion capture data

Multimedia Systems - Package RMoCap is an advanced open-source tool for scientists, engineers and computer graphics familiar with R language who work with motion capture (MoCap) technology. Package...     

Effective and efficient similarity searching in motion capture data

Multimedia Tools and Applications - Motion capture data describe human movements in the form of spatio-temporal trajectories of skeleton joints. Intelligent management of such complex data is a...     

Human motion capture data retrieval based on semantic thumbnail

We present a method for the efficient retrieval and browsing of immense amounts of realistic 3D human body motion capture data. The proposed method organizes motion capture data based on statistical K-means (SK–means), democratic decision making, unsupervised learning, and visual key frame extraction, thus achieving intuitive retrieval by browsing thumbnails of semantic key frames. We apply three steps for the efficient retrieval of motion capture data. The first is obtaining the basic type clusters by clustering motion capture data using the novel SK-means algorithm, and after which, immediately performing character matching. The second is learning the retrieval information of users during the retrieval process and updating the successful retrieval rate of each data; the search results are then ranked on the basis of successful retrieval rate by democratic decision making to improve accuracy. The last step is generating thumbnails with semantic generalization, which is conducted by using a novel key frame extraction algorithm based on visualized data analysis. The experiment demonstrates that this method can be utilised for the efficient organization and retrieval of enormous motion capture data.     

A new approach for generating efficient sample from market basket data

Classical data mining algorithms require expensive passes over the entire database to generate frequent items and hence to generate association rules. With the increase in the size of database, it is becoming very difficult to handle large amount of data for computation. One of the solutions to this problem is to generate sample from the database that acts as representative of the entire database for finding association rules in such a way that the distance of the sample from the complete database is minimal. Choosing correct sample that could represent data is not an easy task. Many algorithms have been proposed in the past. Some of them are computationally fast while others give better accuracy. In this paper, we present an algorithm for generating a sample from the database that can replace the entire database for generating association rules and is aimed at keeping a balance between accuracy and speed. The algorithm that is proposed takes into account the average number of small, medium and large 1-itemset in the database and average weight of the transactions to define threshold condition for the transactions. Set of transactions that satisfy the threshold condition is chosen as the representative for the entire database. The effectiveness of the proposed algorithm has been tested over several runs of database generated by IBM synthetic data generator. A vivid comparative performance evaluation of the proposed technique with the existing sampling techniques for comparing the accuracy and speed has also been carried out.     

A distributed computing tool for generating neural simulation databases

After developing a model neuron or network, it is important to systematically explore its behavior across a wide range of parameter values or experimental conditions, or both. However, compiling a very large set of simulation runs is challenging because it typically requires both access to and expertise with high-performance computing facilities. To lower the barrier for large-scale model analysis, we have developed NeuronPM, a client/server application that creates a "screen-saver" cluster for running simulations in NEURON (Hines & Carnevale, 1997). NeuronPM provides a user-friendly way to use existing computing resources to catalog the performance of a neural simulation across a wide range of parameter values and experimental conditions. The NeuronPM client is a Windows-based screen saver, and the NeuronPM server can be hosted on any Apache/PHP/MySQL server. During idle time, the client retrieves model files and work assignments from the server, invokes NEURON to run the simulation, and returns results to the server. Administrative panels make it simple to upload model files, define the parameters and conditions to vary, and then monitor client status and work progress. NeuronPM is open-source freeware and is available for download at http://neuronpm.homeip.net . It is a useful entry-level tool for systematically analyzing complex neuron and network simulations.     

运动捕捉数据中足迹的谱聚类检测方法

目的 从运动捕捉数据中直接检测足迹非常困难。目前,已有的方法不能在无人工交互条件下实现对任意平地运动数据的足迹自动检测,为此提出一种基于谱聚类的足迹自动检测方法,可应用于任意角色的平地运动。方法 首先,提取角色的脚部运动特征并表示为样本。然后,分析样本模长的变化规律并自适应计算谱聚类参数。最后,使用谱聚类方法检测出足迹帧。结果 实验应用于混合运动数据集后,足迹检测的准确性良好,检测的整体准确率可达98.72%。结论 对实验结果的分析以及与基准线法的比较,证明了本文方法的普适性和有效性。