Passive RFID tags have been widely utilized for object tracking in indoor environment due to their low cost and convenience for deployment. The RFID readings gathered from real world are often noisy. Existing approaches for tracking objects with noisy RFID readings are mostly based on using Particle Filter (PF). However, continuous execution of particle filter will suffer from high computational cost on resource constrained RFID-enabled devices. In this paper, we propose a hybrid method for tracking mobile objects with high accuracy and low computational cost. This is achieved by an adaptively switching between using WCL (Weighted Centroid Localization) and PF according to the estimated velocity of the moving object. We have evaluated the performance of our hybrid method through extensive simulations. We have also validated the performance results by implementing the method in two applications, namely, indoor wheelchair navigation and in-station LRV (Light Rail Vehicle) tracking in one of the Hong Kong MTR depots. The result shows that our proposed method outperforms both WCL and PF in either accuracy or computational cost.