The appearance of individuals captured by multiple non-overlapping cameras varies greatly due to pose and illumination changes between camera views. In this paper we address the problem of dealing with illumination changes in order to recover matching of individuals appearing at different camera sites. This task is challenging as accurately mapping colour changes between views requires an exhaustive set of corresponding chromatic brightness values to be collected, which is very difficult in real world scenarios. We propose a Cumulative Brightness Transfer Function (CBTF) for mapping colour between cameras located at different physical sites, which makes better use of the available colour information from a very sparse training set. In addition we develop a bi-directional mapping approach to obtain a more accurate similarity measure between a pair of candidate objects. We evaluate the proposed method using challenging datasets obtained from real world distributed CCTV camera networks. The results demonstrate that our bi-directional CBTF method significantly outperforms existing techniques.
A cumulative brightness transfer function (CBTF) is proposed by Prosser et al. REF for mapping color between cameras using sparse training set.
Multi-camera matching using bi-directional cumulative brightness transfer functions
in BMVC
Computer Science