Object re-identification and tracking in non-overlapping cameras is a challenging problem due to the variation of the object's appearance, linked to the different view angles, distance and color variation in different cameras. We present a computationally efficient real time human tracking 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's FOV. 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 transfer function (BTF) using Receiver Operating Characteristic (ROC) curves. We propose some modifications in cumulative brightness transfer function (CBTF) which significantly improve the objects re-identification in non-overlapping multi camera environments.