Visual object tracking for surveillance applications is an important task in computer vision. Many algorithms and technologies have been developed to automatically monitor pedestrians, traffic or other moving objects. One main difficulty in object tracking, among many others, is to choose suitable features and models for recognizing and tracking the target. The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although it is important to adapt the mean-shift kernel to handle changes in illumination for robot vision at outdoor site, there is presently no clean mechanism for doing this. This paper presents a novel approach for color tracking that is robust to illumination changes for robot vision. We use two interleaved mean-shift procedures to track the spatial location and illumination intensity of a blob in an image. The proposed method is a new tracking that is robust to lighting changes for robot vision. We use two interleaved mean-shift procedures to track the spatial location and illumination intensity of a blob in an image. We show that our method enables real-time tracking of a color blob for varying lighting conditions.