Real-time Upper-body Human Pose Estimation using a Depth Camera

 
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Old 11-23-2010
Real-time Upper-body Human Pose Estimation using a Depth Camera

HPL-2010-190 Real-time Upper-body Human Pose Estimation using a Depth Camera - Jain, Himanshu Prakash; Subramanian, Anbumani
Keyword(s): Haar cascade based detection, template matching, weighted distance transform and pose estimation
Abstract: Automatic detection and pose estimation of humans is an important task in Human- Computer Interaction (HCI), user interaction and event analysis. This paper presents a model based approach for detecting and estimating human pose by fusing depth and RGB color data from monocular view. The proposed sy ...
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pcg(4rheolef)							    rheolef-6.1 						     pcg(4rheolef)

NAME
pcg -- conjugate gradient algorithm. SYNOPSIS
template <class Matrix, class Vector, class Preconditioner, class Real> int pcg (const Matrix &A, Vector &x, const Vector &b, const Preconditioner &M, int &max_iter, Real &tol, odiststream *p_derr=0); EXAMPLE
The simplest call to 'pcg' has the folling form: size_t max_iter = 100; double tol = 1e-7; int status = pcg(a, x, b, EYE, max_iter, tol, &derr); DESCRIPTION
pcg solves the symmetric positive definite linear system Ax=b using the Conjugate Gradient method. The return value indicates convergence within max_iter (input) iterations(0), or no convergence within max_iter iterations(1). Upon suc- cessful return, output arguments have the following values: x approximate solution to Ax = b max_iter the number of iterations performed before the tolerance was reached tol the residual after the final iteration NOTE
pcg is an iterative template routine. pcg follows the algorithm described on p. 15 in @quotation Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 2nd Edition, R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo, C. Romine, H. Van der Vorst, SIAM, 1994, ftp.netlib.org/templates/tem- plates.ps. @end quotation The present implementation is inspired from IML++ 1.2 iterative method library, http://math.nist.gov/iml++. IMPLEMENTATION
template <class Matrix, class Vector, class Vector2, class Preconditioner, class Real, class Size> int pcg(const Matrix &A, Vector &x, const Vector2 &Mb, const Preconditioner &M, Size &max_iter, Real &tol, odiststream *p_derr = 0, std::string label = "cg") { Vector b = M.solve(Mb); Real norm2_b = dot(Mb,b); if (norm2_b == Real(0)) norm2_b = 1; Vector Mr = Mb - A*x; Real norm2_r = 0; if (p_derr) (*p_derr) << "[" << label << "] #iteration residue" << std::endl; Vector p; for (Size n = 0; n <= max_iter; n++) { Vector r = M.solve(Mr); Real prev_norm2_r = norm2_r; norm2_r = dot(Mr, r); if (p_derr) (*p_derr) << "[" << label << "] " << n << " " << ::sqrt(norm2_r/norm2_b) << std::endl; if (norm2_r <= sqr(tol)*norm2_b) { tol = ::sqrt(norm2_r/norm2_b); max_iter = n; return 0; } if (n == 0) { p = r; } else { Real beta = norm2_r/prev_norm2_r; p = r + beta*p; } Vector Mq = A*p; Real alpha = norm2_r/dot(Mq, p); x += alpha*p; Mr -= alpha*Mq; } tol = ::sqrt(norm2_r/norm2_b); return 1; } rheolef-6.1 rheolef-6.1 pcg(4rheolef)