With the increase of the number of cameras installed across a video surveillance network, the ability of security staffs to attentively scan all the video feeds actually decreases. Therefore, the need for an intelligent system that operates as a tracking system is vital for security personnel to do their jobs well. Tracking people as they move through a camera network with non-overlapping field of view is a challenging issue. It requires re-identification of objects when they leaves one field of view and appears at another scene later. The appearance of the people is changed significantly among the cameras due to illumination changes, parameters of camera, viewing angle, and deformable geometry of people. Additionally the observations of people are often broadly separated in time and space and common proximity techniques can not be used to constrain possible correspondences. In this paper a new feature was proposed to represent the appearance of people, which is capable of dealing with the common illumination changes occurring in indoor environment. To produce the proposed feature, first the image was transformed to YCbCr color space. Then diagonal vectors of color co-occurrence matrix of each individual were extracted and normalized. Experimental results from a real surveillance scene showed the efficiency of the proposed method.