Implementation of augmented reality and object tracking using multiple camera

2011 ◽  
Vol 16 (6) ◽  
pp. 89-97
Author(s):  
Hag-Hee Kim
Keyword(s):  
Augmented Reality ◽  
Object Tracking ◽  
Multiple Camera

Object tracking using multiple camera video streams

2010 ◽  
Author(s):  
Mehrube Mehrubeoglu ◽  
Diego Rojas ◽  
Lifford McLauchlan
Keyword(s):  
Object Tracking ◽  
Video Streams ◽  
Multiple Camera

scholarly journals Comparison of Tracking Techniques on 360-Degree Videos

2019 ◽  
Vol 9 (16) ◽  
pp. 3336 ◽  
Author(s):  
Tzu-Wei Mi ◽  
Mau-Tsuen Yang
Keyword(s):  
Augmented Reality ◽  
Object Tracking ◽  
Ground Truth ◽  
Physical Object ◽  
Future Research ◽  
Scale Change ◽  
Tracking Algorithms ◽  
Viewpoint Change ◽  
Pros And Cons

With the availability of 360-degree cameras, 360-degree videos have become popular recently. To attach a virtual tag on a physical object in 360-degree videos for augmented reality applications, automatic object tracking is required so the virtual tag can follow its corresponding physical object in 360-degree videos. Relative to ordinary videos, 360-degree videos in an equirectangular format have special characteristics such as viewpoint change, occlusion, deformation, lighting change, scale change, and camera shakiness. Tracking algorithms designed for ordinary videos may not work well on 360-degree videos. Therefore, we thoroughly evaluate the performance of eight modern trackers in terms of accuracy and speed on 360-degree videos. The pros and cons of these trackers on 360-degree videos are discussed. Possible improvements to adapt these trackers to 360-degree videos are also suggested. Finally, we provide a dataset containing nine 360-degree videos with ground truth of target positions as a benchmark for future research.

Comparison of Natural Feature Descriptors for Rigid-Object Tracking for Real-Time Augmented Reality

2014 ◽  
Author(s):  
Francely Franco Bermudez ◽  
Christian Santana Diaz ◽  
Sheneeka Ward ◽  
Rafael Radkowski ◽  
Timothy Garrett ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Object Tracking ◽  
Real Time ◽  
Virtual Object ◽  
Interest Points ◽  
Rigid Object ◽  
Binary Descriptors ◽  
Rigid Objects ◽  
Natural Feature ◽  
Feature Descriptors

This paper presents a comparison of natural feature descriptors for rigid object tracking for augmented reality (AR) applications. AR relies on object tracking in order to identify a physical object and to superimpose virtual object on an object. Natural feature tracking (NFT) is one approach for computer vision-based object tracking. NFT utilizes interest points of a physcial object, represents them as descriptors, and matches the descriptors against reference descriptors in order to identify a phsical object to track. In this research, we investigate four different natural feature descriptors (SIFT, SURF, FREAK, ORB) and their capability to track rigid objects. Rigid objects need robust descriptors since they need to describe the objects in a 3D space. AR applications are also real-time application, thus, fast feature matching is mandatory. FREAK and ORB are binary descriptors, which promise a higher performance in comparison to SIFT and SURF. We deployed a test in which we match feature descriptors to artificial rigid objects. The results indicate that the SIFT descriptor is the most promising solution in our addressed domain, AR-based assembly training.

Object Tracking over a Multiple-Camera Network

2015 ◽  
Author(s):  
Zhenzhong Chen ◽  
Weihang Liao ◽  
Bin Xu ◽  
Hongyi Liu ◽  
Qisheng Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Camera Network ◽  
Multiple Camera
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