Optimal camera placement for motion capture systems in the presence of dynamic occlusion

Optical motion capture is based on estimating the three-dimensional positions of markers by triangulation from multiple cameras. Successful performance depends on points being visible from at least two cameras and on the accuracy of the triangulation. Triangulation accuracy is strongly related to the positions and orientations of the cameras. Thus, the configuration of the camera network has a critical impact on performance. A poor camera configuration may result in a low quality three-dimensional (3D) estimation and consequently low quality of tracking. This paper proposes a camera configuration metric that is based on a probabilistic model of target point visibility from cameras with "good" views. An efficient algorithm, based on simulated annealing, is introduced for estimating the optimal configuration of an arbitrary set of cameras for a given distribution of target points. The accuracy and robustness of the algorithm are evaluated through both simulation and empirical measurement. An implementation of the method is available for download as a tool for the community [Rahimian and Kearney 2015] (Figure 1).

[1]  Peter Kovesi,et al.  Automatic Sensor Placement from Vision Task Requirements , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[3]  Ernest L. Hall,et al.  Error Analysis For A Two-Camera Stereo Vision System , 1986, Other Conferences.

[4]  Hamid Aghajan,et al.  A Process for Sensor Configuration in Multi-Camera Networks , 2010 .

[5]  David B. Fogel,et al.  Evolution-ary Computation 1: Basic Algorithms and Operators , 2000 .

[6]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[7]  Alexandru Tupan,et al.  Triangulation , 1997, Comput. Vis. Image Underst..

[8]  Samir W. Mahfoud.  Boltzmann selection , 2018, Evolutionary Computation 1.

[9]  Dani Lischinski,et al.  Automatic camera placement for image-based modeling , 1999, Proceedings. Seventh Pacific Conference on Computer Graphics and Applications (Cat. No.PR00293).

[10]  Lester Ingber,et al.  Simulated annealing: Practice versus theory , 1993 .

[11]  Edgar N. Reyes,et al.  Optimization using simulated annealing , 1998, Northcon/98. Conference Proceedings (Cat. No.98CH36264).

[12]  R. Hartley.  Triangulation, Computer Vision and Image Understanding , 1997 .

[13]  John N. Sanders-Reed,et al.  Error propagation in two-sensor three-dimensional position estimation , 2001 .

[14]  S. Mason.  Heuristic reasoning strategy for automated sensor placement , 1997 .

[15]  T. C. Shermer,et al.  Recent results in art galleries (geometry) , 1992, Proc. IEEE.

[16]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  John N. Sanders-Reed.  Impact of tracking system knowledge on multisensor 3D triangulation , 2002, SPIE Defense + Commercial Sensing.

[18]  Xing Chen,et al.  An occlusion metric for selecting robust camera configurations , 2008, Machine Vision and Applications.

[19]  Thomas S. Huang,et al.  Analysis of uncertainty bounds due to quantization for three-dimensional position estimation using multiple cameras , 1998 .

[20]  John C. Hart,et al.  The CAVE: audio visual experience automatic virtual environment , 1992, CACM.

[21]  Reinhard Männer,et al.  An Efficient and Accurate Method for 3D-Point Reconstruction from Multiple Views , 2005, International Journal of Computer Vision.

[22]  John N. Sanders-Reed.  Triangulation Position Error Analysis for Closely Spaced Imagers , 2002 .

[23]  J. O'Rourke.  Art gallery theorems and algorithms , 1987 .

[24]  Roger Mohr,et al.  Optimal camera placement for accurate reconstruction , 2002, Pattern Recognit..

[25]  T. Shermer.  Recent Results in Art Galleries , 1992 .