Eye tracking in near-eye displays

A method for tracking eye movement in a near-eye display, comprising:

• identifying a plurality of base images of an object or a portion thereof;
• generating a search image based at least in part upon at least some of the plurality of base images;
• generating a deep learning result at least by performing a deep learning process on a base image using a neural network in a deep learning model;
• and
• localizing a captured image to the search image at least by performing an image registration process on the captured image and the search image using a Kalman filter model and the deep learning result,
• wherein localizing the captured image comprises:
• embedding the deep learning process into a state transition model;
• and
• receiving the deep learning result at the Kalman filter model as one or more measurements using at least the state transition model, where the state transition model is used to determine a next position state at a next time point based at least in part upon a prior position state at a prior time point and at least one of process noise or a control vector of a state estimation model.