Tracking feature extraction based on manifold learning framework

摘要

Abstract Manifold learning is a fast growing area of research recently. The main purpose of manifold learning is to search for intrinsic variables underlying high-dimensional inputs which lie on or are close to a low-dimensional manifold. Different from current theoretical works and applications of manifold learning approaches, in our work manifold learning framework is transferred to tracking feature extraction for the first time. The contributions of this article include three aspects. Firstly, in this article, we focus on tracking feature extraction for dynamic visual tracking on dynamic systems. The feature extracted in this article is based on manifold learning framework and is particular for dynamic tracking purpose. It can be directly applied to system control of dynamic systems. This is different from most traditional tracking features which are used for recognition and detection. Secondly, the proposed tracking feature extraction method has been successfully applied to three different dynamic systems: dynamic robot system, intelligent vehicle system and aircraft visual navigation system. Thirdly, experimental results have proven the validity of the tracking method based on manifold learning framework. Particularly, in the tracking experiments the vision system is dynamic. The tracking method is also compared with the well-known mean-shift tracking method, and tracking results have shown that our method outperforms the latter. Keywords: visual trackingrobotic visual trackingfeature extraction Acknowledgements The work of the first author (HQ) was partly supported by the Chinese Academy of Sciences through the Hundred Talents Program, the NNSF of China under grant 60675039 and grant 60621001, the 863 Program of China under grant 2006AA04Z217 and the Outstanding Youth Fund of the NNSF of China under grant 60725310. The third author (BZ) was partly supported by the Chinese Academy of Sciences through the Hundred Talents Program, the 863 Program of China under grant 2007AA04Z228, the 973 Program of China under grant 2007CB311002 and the NNSF of China under grant 90820007.