Maximum Likelihood Estimation for Multiple Camera Target Tracking on Grassmann Tangent Subspace

IEEE Transactions on Cybernetics
Mojtaba Amini-OmamSeyed Ali Ghorashi

Abstract

In this paper, we introduce a likelihood model for tracking the location of object in multiple view systems. Our proposed model transforms conventional nonlinear Euclidean estimation model to an estimation model based on the manifold tangent subspace. In this paper, we show that by decomposition of input noise into two parts and description of model by exponential map, real observations in the Euclidean geometry can be transformed to the manifold tangent subspace. Moreover, by obtained tangent subspace likelihood function, we propose two iterative and noniterative maximum likelihood estimation approaches which numerical results show their good performance.

References

Dec 18, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·François FleuretPascal Fua
Jun 17, 2010·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Bi SongAmit K Roy-Chowdhury
Feb 2, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Jerome BerclazPascal Fua
Jun 13, 2013·IEEE Transactions on Cybernetics·Zulfiqar Hasan Khan, Irene Yu-Hua Gu
Apr 12, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Ming DuRama Chellappa
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yi WuMing-Hsuan Yang

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