Finding an adequate escape pod to real time Augmented Reality applications (original) (raw)

A real-time tracker for markerless augmented reality

The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings., 2003

Augmented Reality has now progressed to the point where real-time applications are being considered and needed. At the same time it is important that synthetic ele-ments are rendered and aligned in the scene in an accurate and visually acceptable way. In order to address these is- ...

Marker Detection and Tracking for Augmented Reality Applications

—This paper explores a simple method for detecting and tracking " fiducial " markers in a webcam video stream. The system first uses SIFT feature matching to detect when a marker is present in a frame of the video stream. Then, the detected keypoints are given to a KLT optical flow tracker, which tracks the keypoints frame-by-frame as they move through the video. In this paper, we will describe the method in detail, and present our results and analysis.

Markerless Tracking for Augmented Reality Using Different Classifiers

Augmented reality (AR) is the combination of a real scene viewed by the user and a virtual scene generated by the computer that augments the scene with additional information. The user of an AR application should feel that the augmented object is a part of the real world. One of the factors that greatly affect this condition is the tracking technique used. In this paper, an augmented reality application is adopted with markerless tracking as a classification task. ORB algorithm is used for feature detection and the FREAK algorithm is used for feature description. The classifiers used for the tracking task are KNN, Random Forest, Extremely Randomized Trees, SVM and Bayes classifier. The performance of each classifier used is evaluated in terms of speed and efficiency. It has been observed that KNN outperforms other classifiers including Random Forest with different number of trees.

A Survey on Augmented Reality Challenges and Tracking

Acta Graphica, 2016

This survey paper presents a classification of different challenges and tracking techniquesin the field of augmented reality. The challenges in augmented reality arecategorized into performance challenges, alignment challenges, interaction challenges,mobility/portability challenges and visualization challenges. Augmentedreality tracking techniques are mainly divided into sensor-based tracking, visionbasedtracking and hybrid tracking. The sensor-based tracking is further dividedinto optical tracking, magnetic tracking, acoustic tracking, inertial tracking or anycombination of these to form hybrid sensors tracking. Similarly, the vision-basedtracking is divided into marker-based tracking and markerless tracking. Eachtracking technique has its advantages and limitations. Hybrid tracking provides arobust and accurate tracking but it involves financial and tehnical difficulties.