动作捕捉浅析(二)——光学动作捕捉中英文资料

1、动作捕捉浅析（一）光学动作捕捉一、光学式：光学式运动捕捉通过对目标上特定光点的监视和跟踪来完成运动捕捉的任务。目前常见的光学式运动捕捉大多基于计算机视觉原理。从理论上说，对于空间中的一个点，只要它能同时为两部相机所见，则根据同一时刻两部相机所拍摄的图像和相机参数，可以确定这一时刻该点在空间中的位置。当相机以足够高的速率连续拍摄时，从图像序列中就可以得到该点的运动轨迹；二、资料中文资料：PS光学式人体运动捕捉系统是目前光学式当中的价格最便宜，性能最好的系统。目前它具有世界上性能价格比最高的特点，被广泛应用于游戏制作、步态分析，运动医学及康复治疗，运动分析，人体工程学研究、模拟训练、生物力学研究等

2、领域。客户为大学，科研单位，体育研究所，实验室等。   和机械电子式动作捕捉系统有很大的区别。一元硬币大小的发光二极管，依靠这些主动方式的发光二极管可以快速，高精度，方便的获取人体各个部位的运动数据。不同于被动式的光学反射标志。 PS系统的主要特点 独特性 能够实时获取多达120个LED主动方式标志点的运动轨迹。相比传统的被动方式标志点的光学运动捕捉系统，具有许多良好的性能。 采用具有发明专利权的主动式LED标志，每个标志是唯一的，因此很好的解决了运动标志点的错位问题。 即使某一个标志点LED脱落，系统依然会识别。高精度 3600 x 3600 像素（1296万像

3、素）。通过使用“子像素“技术，系统的像素数可达到30000 x 30000像素。高速度 采集数据的速度为480次/每秒。PS系统可以同时采集120个LED标志点的数据。实时性 大部分运动轨迹跟踪系统都存在延迟问题。因为需要进行数据的过滤处理，消除不规则数据等。但是PS由于采用主动式LED标识，轨迹数据不存在噪音，因此不需要进行任何数据再处理。高性价比 通常12个摄像头的光学系统的价格是25万美金，有的甚至60万美金。PS系统的价格远远低于这些系统。PS硬件构成计算机工作站计算机工作站该计算机工作站能够采集1到24个摄像机的数据，并且能够获取每秒钟480回的空间位置数据。 物理尺寸(厘米)：32

4、.3 x 22.2 x 21.6重量（公斤）：5.6发光二极管基站和计算机工作站同步控制发光二极管，频率为2.4GH。该基站能够向外输出时间同步信号。比如：GEN-LOCK和IRIG-B。物理尺寸(厘米)：12.7 x 7.6 x 3.0重量（公斤）：0.16脉冲摄像机3600x3600像素（1296万像素） 16位动态范围的行扫描 30000x30000子像素 480次扫描/每秒 物理尺寸(厘米)：10.8 x 19.3 x 7.6重量（公斤）：0.08发光二极管控制器1到64个发光二极管的控制器，自备电源，连续工作时间可达2到4个小时，最长可达8个小时。标准动作捕捉系统需要使用4个控制器。

5、物理尺寸(厘米)：12.7 x 6.9 x 2.1重量（公斤）：0.1 发光二极管发光频率为30到480Hz, 红色发光二极管。客户可选择红色，蓝色，或者黄色。物理尺寸(厘米)：2.0 x 1.4 x 0.3重量（公斤）：0.006 英文资料：Optical Motion CaptureYiannis Aloimonos and Gutemberg Guerra-FilhoComputer Vision LaboratoryCenter for Automation ResearchInstitute for Advanced Computer StudiesDepartment o

6、f Computer ScienceUniversity of MarylandCollege Park, Maryland 20742-3275, USA Motion capture is the process of recording real life movement of a subject as sequences of Cartesian coordinates in 3D space. Optical motion capture (OMC) uses cameras to reconstruct the body posture of the performer

7、. One approach employs a set of multiple synchronized cameras to capture markers placed in strategic locations on the body. A motion capture system has applications in computer graphics for character animation, in virtual reality for human control-interface, and in video games for realistic simulati

8、on of human motion. In this tutorial, we discuss the theoretical and empirical aspects of an optical motion capture system. Basically, for a motion capture system implementation; the resources required consist of a number of synchronized cameras, an image acquisition system, a capturing area, and a

9、special suit with markers. The locations of the markers on the suit are designed such that the required body parts (e.g. joints) are covered. We present our motion capture system using a framework that identifies different sub-problems to be solved in a modular way. The sub-problems involved in OMC

10、are initialization, marker detection, spatial correspondence, temporal correspondence, and post-processing. In this tutorial, we discuss the theory involved in each sub-problem and the corresponding novel techniques used in the current implementation. The initialization includes setting up a human m

11、odel and the computation of intrinsic and extrinsic camera calibration. Marker detection involves finding the 2D pixel coordinates of markers in the images. The spatial correspondence problem consists in finding pairs of detected markers in different images captured at the same time with different v

12、iewpoints such that each pair corresponds to the projections of the same scene point. Given camera calibration and the spatial matching, the 3D reconstruction of markers (translational data) is achieved by triangulating the various camera views. The temporal correspondence problem (tracking) involve

13、s matching two clouds of 3D points representing detected markers at two consecutive frames, respectively. The temporal correspondence module builds a track for each marker where the markers 3D coordinates are concatenated according to time. Post-processing consists in labeling each track with a mark

14、er code, finding missing markers lost by occlusions, correcting possible gross errors, and filtering noise. Once the translational data is processed, a hierarchical human model may be used to compute rotational data (joint angles). We consider standard data formats available for motion capture data

15、(e.g. bvh, acclaim). Other important techniques used to improve consistency in the motion data are volumetric reconstruction, inverse kinematics, and inverse dynamics. We also cover topics related to editing and manipulation of motion data.Tutorial SlidesThe Language of Human Movement · &#

16、160;Outline ·        Introductiono o     Realistic Movement: Synthesis and Analysiso o     Motion Capture Technologieso o     Applications·        Required Resourceso o&#

17、160;    Capture Roomo o     Body Suito o     Camera Equipmento o     Acquisition System·        Initializationo o     Markers Configurationo o     Cam

18、era Calibrationo o     World Coordinate System Alignmento o     Background Subtractiono o     Kinematic Human Body Model·        Marker/Feature Detectiono o     Edgeso o  

19、0;  Cornerso o     SIFT Features·        Spatial Correspondenceo o     Stereo Matchingo o     Wide Baselineo o     Dense Correspondenceo o     Triangulation

20、3;        Temporal Correspondenceo o     Tracking with Appearanceo o     2D and 3D Tracking·        Post-Processingo o     Labelingo o     Missing Markerso

21、 o     Rigidity Testo o     Motion Data Filteringo o     Translational and Rotational Datao o     Data ·        Advanced Topicso o     Visual Hull Reconstructiono o&

22、#160;    Monocular Markerless MoCap · Original Multiview Video One approach employs a set of multiple synchronized cameras to capture markers placed in strategic locations on the body. The original videos for the human activities jump and tiptoe are presented in videos 1a an

23、d 1b, respectively.Video 1a: Original jump action.Video 1b: Original tiptoe action.· Marker Detection Marker detection involves finding the 2D pixel coordinates of markers in the images. In our system, the subject wears a black suit with white markers in a squared shape. Red circles represent t

24、he markers detected by our system in videos 2a and 2b. Video 2a: Markers detected in jump action.Video 2b: Markers detected in tiptoe action.· Spatial Correspondence The spatial correspondence problem consists in finding pairs of detected markers in different images captured at the same ti

25、me with different viewpoints such that each pair corresponds to the projections of the same scene point. The pairs of markers computed by our system are displayed in videos 3a and 3b. The matches are represented by disparity vectors for markers in consecutive cameras. Video 3a: Disparity vector

26、s in jump action.Video 3b: Disparity vectors in tiptoe action.· 3D Reconstruction Given camera calibration and the spatial matching, the 3D reconstruction of markers (translational data) is achieved by triangulating the various camera views. The reconstructed points are shown in videos 4a and 4

27、b, where the points are virtually inserted in the original background. In videos 5a and 5b, the reconstructed points are projected into different viewpoints. Video 4a: 3D points in the original background (jump action).Video 4b: 3D points in the original background (tiptoe action). Video 5a: 3D points from different viewpoints (jump action).Video 5b: 3D points from different viewp