Object re-identification in non-overlapping cameras is a challenging problem due to variation of the object, linked to the different view angle, distance and color calibration of different cameras. We propose a computationally efficient real time human re-identification algorithm, which can track objects inside the field of view (FOV) of a camera, re-identify objects that exit and then return in a same or in a different camera FOV. This paper is based on our 1-D appearance model proposed in previous works, called vertical feature (VF), which is view and size invariant and that is used for object recognition and re-identification. But the object appearance in several cameras may be very different due to illumination conditions, camera gain, focus, focal length etc. Therefore inter-camera color calibration is important for object re-identification before applying object recognition features. We compare different existing color calibration methods and evaluate their color Brightness Function (BTF) and propose some modification in cumulative Brightness Transfer Function (CBTF), which improves the objects re-identification significantly in non-overlapping multi camera environments.