Object tracking, a basic problem in computer vision, has a wide range of application scenarios. Due to the advance in the computational capacity of hardware and deep learning methods, conventional deep learning methods for object tracking have higher precision, but they face the problems of massive model parameters and high demand for computational resources and power consumption. In recent years, with the booming development of unmanned aerial vehicle (UAV) and Internet of Things (IoT) applications, a great deal of research focuses on how to achieve real-time tracking in embedded hardware environment with limited storage space and computational capacity and low power consumption. Firstly, object tracking algorithms in the embedded environment, including the ones combining correlation filters with deep learning and those based on lightweight neural networks, are analyzed and discussed. Secondly, deployment procedures of deep learning models and classical embedded object tracking applications, such as those in UAVs, are summarized. Finally, future research directions are given.