Qualitative correspondence for object tracking using dynamic panorama

We propose a novel method to identify the correspondence of objects in different spaces using panorama generation and qualitative reasoning, the spaces are namely image space, camera fuzzy qualitative space and real world space. The correspondence is carried out in a three-layered image understanding framework. The first layer consists of single cameras which is to extract quantitative meausres using off-the-shelf image algorithms with a target of providing local feature information; The second layer targets at fusing qualitative information of single cameras at the level of cameras network; The third layer is intended to generate semantic description of object behaviours using nature language generation. This paper is focused on qualitative correspondence of objects in the first layer, which is realized by a two-stage tracking cycle consisting of panorama generation and object tracking. A case study is given to demonstrate the effectiveness of the method.