Multi-view object matching and tracking using canonical correlation analysis

Multi-view tracking of objects in video surveillance consists in segmenting and automatically following them through different camera views. This may be achieved using geometric methods, e.g. by calibrating camera sensors and using their transformation matrices. However, in practice the precision of calibration is a major issue when trying to achieve this task robustly. In this paper, we present an alternative framework for multi-view object matching and tracking based on canonical correlation analysis. Our method is purely statistical and encodes intrinsic object appearances while being view-point invariant. We will show that our technique is (i) easy-to-set (ii) theoretically well grounded and (iii) provides robust matching and tracking results for traffic surveillance.