Mobile edge computing platforms, such as cloudlets, bring computation resources closer to mobile users, as compared to the cloud, which decreases the end-to-end network latency. This benefit enables a myriad of real-time mobile applications, especially augmented reality (AR), that require low latency and high computation power. However, when mobile users move away from the attached cloudlet, the offloaded services have to be migrated or rebuilt on a new nearby cloudlet. However, this service rebuilding process takes a lot of time and may deteriorate user experience. In this paper, we propose a smart service rebuilding scheme which seamlessly restores the offloading services on the target cloudlet while the mobile user is moving. The service rebuilding process includes the radio handoff stage and service handoff stage. A seamless service rebuilding process is achieved via predicting user's target cloudlet before being triggered a radio handoff, by leveraging extracted features from the captured frames of the mobile user's camera. Furthermore, based on the proposed service rebuilding scheme, we design a feature mapping algorithm to achieve a high prediction precision and a short prediction latency. We implement our scheme on a testbed and conduct experiments using real world AR applications. The experimental results show that our proposed scheme decreases the service rebuilding latency by around 65.8%, as compared to the conventional rebuilding process. In addition, we conduct extensive simulations to evaluate the performance of our proposed feature mapping algorithm. Simulation confirms that our algorithm is robust and can predict users' target cloudlet with high precision and low latency.