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Automated target tracking and recognition using coupled view and identity manifolds for shape representation

Vijay Venkataraman1, Guoliang Fan1*, Liangjiang Yu1, Xin Zhang2, Weiguang Liu3 and Joseph P Havlicek4

Author Affiliations

1 School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA

2 School of Electronics and Information Engineering South China University of Technology, China

3 College of Computer Science, Zhongyuan Univ. of Technology, China

4 School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019 USA

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EURASIP Journal on Advances in Signal Processing 2011, 2011:124  doi:10.1186/1687-6180-2011-124

Published: 7 December 2011


We propose a new couplet of identity and view manifolds for multi-view shape modeling that is applied to automated target tracking and recognition (ATR). The identity manifold captures both inter-class and intra-class variability of target shapes, while a hemispherical view manifold is involved to account for the variability of viewpoints. Combining these two manifolds via a non-linear tensor decomposition gives rise to a new target generative model that can be learned from a small training set. Not only can this model deal with arbitrary view/pose variations by traveling along the view manifold, it can also interpolate the shape of an unknown target along the identity manifold. The proposed model is tested against the recently released SENSIAC ATR database and the experimental results validate its efficacy both qualitatively and quantitatively.

tracking and recognition; shape representation; shape interpolation; manifold learning