In augmented reality, the real world is augmented by virtual, computer generated objects. If real and virtual objects are not aligned properly, the experience of the user will not be satisfying, as the illusion of a common world consisting of both real and virtual objects will not be present. To achieve this precise alignment, it is necessary to track the user’s position and orientation with hi...

In this paper, we propose a new tracking method that uses Gaussian Mixture Model (GMM) and Optical Flow approach for object tracking. The GMM approach consists of three different Gaussian distributions, the average, standard deviation and weight respectively. There are two important steps to establish the background for model, and background updates which separate the foreground and background....

Tracking devices are an essential component of an image-guided surgery system. These devices are used to track the position of instruments relative to the patient anatomy. Although early tracking systems were essentially mechanical digitizers, the field quickly adopted optical tracking systems because of their high accuracy and relatively large workspace. However, optical tracking systems requi...

We employ a prediction model for moving object velocity and location estimation derived from Bayesian theory. The optical flow of a certain moving object depends on the history of its previous values. A joint optical flow estimation and moving object segmentation algorithm is used for the initialization of the tracking algorithm. The segmentation of the moving objects is determined by appropria...

We present a novel, real-time, markerless vision-based tracking system, employing a rigid orthogonal configuration of two pairs of opposing cameras. Our system uses optical flow over sparse features to overcome the limitation of vision-based systems that require markers or a pre-loaded model of the physical environment. We show how opposing cameras enable cancellation of common components of op...

We employ a prediction model for moving object velocity and location estimation derived from Bayesian theory. The optical flow of a certain moving object depends on the history of its previous values. A joint optical flow estimation and moving object segmentation algorithm is used for the initialization of the tracking algorithm. The segmentation of the moving objects is determined by appropria...

The paper tackles the problem of real-time image motion tracking as a baseline function for visual motion estimation. Motion tracking is performed using the principles of 2D area correlation and joint transform correlation. To cope with real-time requirements a compact embedded optical Joint Transform Correlator design is presented, which allows using image motion tracking in mobile application...