TY - GEN

T1 - Minimum Tracking with SPSA and Applications to Image Registration

AU - Granichin, O.

AU - Gurevich, L.

AU - Vakhitov, A.

PY - 2009

Y1 - 2009

N2 - An application of simultaneous perturbation stochastic approximation (SPSA) algorithm with two measurements per iteration to the problem of object tracking on video is discussed. The upper bound of mean square estimation error is determined in case of once differentiable functional and almost arbitrary noises. Weak restrictions on uncertainty allow to use random sampling instead of full pixelwise difference calculation. The experiments show significant increase in performance of object tracking comparing to the classical Lucas-Kanade algorithm. The results can be generalized to improve more recent kernel-based tracking methods.

AB - An application of simultaneous perturbation stochastic approximation (SPSA) algorithm with two measurements per iteration to the problem of object tracking on video is discussed. The upper bound of mean square estimation error is determined in case of once differentiable functional and almost arbitrary noises. Weak restrictions on uncertainty allow to use random sampling instead of full pixelwise difference calculation. The experiments show significant increase in performance of object tracking comparing to the classical Lucas-Kanade algorithm. The results can be generalized to improve more recent kernel-based tracking methods.

KW - STOCHASTIC-APPROXIMATION

KW - ALGORITHM

KW - PERTURBATION

M3 - статья в сборнике материалов конференции

SN - 978-989-674-004-7

SP - 66

EP - 74

BT - NESTER 2009: NETWORKED EMBEDDED AND CONTROL SYSTEM TECHNOLOGIES: EUROPEAN AND RUSSIAN R&D COOPERATION, PROCEEDINGS

A2 - Pascoal, A

A2 - Ufnarovsky, null

PB - Institute for Systems and Technologies of Information, Control and Communication

Y2 - 4 July 2009 through 5 July 2009

ER -