Object tracking in complex backgrounds with dramatic appearance variations is a challenging problem in computer vision. We tackle this problem by a novel approach that incorporates a deep learning architecture with an on-line AdaBoost framework. Inspired by its multi-level feature learning ability, a stacked denoising autoencoder (SDAE) is used to learn multi-level feature descriptors from a set of auxiliary images. Each layer of the SDAE, representing a different feature space, is subsequently transformed to a discriminative object/background deep neural network (DNN) classifier by adding a classification layer. By an on-line AdaBoost feature selection framework, the ensemble of the DNN classifiers is then updated on-line to robustly distinguish the target from the background. Experiments on an open tracking benchmark show promising results of the proposed tracker as compared with several state-of-the-art approaches.