In the Object Tracking and Localization course on Udacity, you will learn how to track objects programmatically over time. You will understand motion models that define object movement and analyze videos as sequences of image frames. Additionally, you will learn about optical flow and feature matching to track a set of unique features, such as matching features from one image frame to another and tracking a moving car. The course also covers robot localization using Bayesian statistics and sensor measurements for safe navigation. You will implement a histogram filter for robot localization in Python. Furthermore, you will learn about Graph SLAM to identify landmarks and create a map of an environment while simultaneously localizing an autonomous vehicle. The course project focuses on landmark detection and tracking, where you will use feature detection and keypoint descriptors to build a map of the environment with SLAM. You will also implement a robust method for tracking an object over time using probability, motion models, and linear algebra. This project will test your knowledge of localization techniques commonly used in autonomous vehicle navigation.