Sparse Feature Tracking

Sparse Feature Tracking is a technique used in computer vision to identify and follow specific points or features in images or videos over time. Instead of analyzing every pixel, it focuses on a smaller number of distinct features—like corners or edges—making it faster and more efficient. This method is particularly useful for tasks such as motion tracking, where the movement of these features is used to understand how objects move within a scene, enabling applications like video stabilization, augmented reality, and robotics. It balances detail with efficiency, capturing essential information without unnecessary data overload.