Tracking feature extraction based on manifold learning framework

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.