Automatic detection and tracking of objects get more important with the increasing number of surveillance cameras and mobile platforms having cameras. Tracking systems are either designed with stationary camera or designed to work in moving camera. When the camera is stationary, correspondence based tracking with background subtraction has a number of benefits such as enabling automatic detection of new objects in the scene and better tracking accuracy. On the other hand, mean shift is a histogram-based tracking method which is suitable for tracking objects under unconstrained scenarios like moving camera. However, with mean shift, the objects to be tracked cannot be detected automatically, only existing or manually selected objects can be tracked. In this paper, we propose a dual-mode system which combines the advantages of correspondence based tracking and mean shift tracking. A reliability measure based on background update rate is calculated for each frame. Under normal operating conditions, when the background estimation is working reliably, correspondence based tracking is used. When the reliability of background estimation becomes low, due to moving camera, the system automatically switches to mean shift tracking until the reliability of background information increases again. The results show that the system can detect new objects and track them reliably using background subtraction. Even though the background subtraction based systems detect high number of false objects when the camera starts moving, the proposed system hands over the tracked objects to mean shift tracker and avoids detection of false objects and enables uninterrupted tracking.