Real-Time Object Tracking for Augmented Reality Combining Graph Cuts and Optical Flow

2007 ◽  
Author(s):  
Jonathan Mooser ◽  
Suya You ◽  
Ulrich Neumann
Keyword(s):  
Augmented Reality ◽  
Optical Flow ◽  
Object Tracking ◽  
Real Time ◽  
Graph Cuts

scholarly journals Optical flow background estimation for real-time pan/tilt camera object tracking

Measurement ◽  
2014 ◽  
Vol 48 ◽  
pp. 195-207 ◽  
Author(s):  
Daniel D. Doyle ◽  
Alan L. Jennings ◽  
Jonathan T. Black
Keyword(s):  
Optical Flow ◽  
Object Tracking ◽  
Real Time ◽  
Background Estimation

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.

scholarly journals Implementasi Model Mobile Augmented Reality e-Booklet untuk Mempromosikan Object Wisata Unggulan Provinsi Riau dengan metode 3D Object Tracking

2016 ◽  
Vol 1 (2) ◽  
pp. 177
Author(s):  
Unang Rio ◽  
Susi Erlinda ◽  
Dwi Haryono
Keyword(s):  
Augmented Reality ◽  
Object Tracking ◽  
Real Time ◽  
Mobile Augmented Reality ◽  
Pass Filter ◽  
3D Object ◽  
High Pass Filter ◽  
High Pass

Intisari  - Provinsi Riau terletak di lokasi yang strategis dan memiliki kekayaan keindahan alam dan budaya  yang  unik,  Riau  menawarkan  banyak  tempat  wisata  baik alam dan  budaya.  Tujuan  dari penelitian ini untuk membuat aplikasi  yang membantu masyarakat  untuk mendapatkan informasi objek wisata unggulan di Provinsi Riau. Salah satu faktor yang menyebabkan kurang berkembangnya sektor pariwisata di Provinsi Riau saat ini adalah karena pengelolaan informasi yang bersifat promosi dan belum mampu memaksimalkan ketersediaan teknologi informasi yang tersedia. Penggunaan teknologi augmented reality adalah teknologi yang menggabungkan benda maya tiga dimensi ke dalam  sebuah  lingkungan  tiga-dimensi  nyata  dan  kemudian  memproyeksikan  benda-benda  maya secara real time. Markerless objek wisata unggulan adalah bono surfing, balap alur, tongkang bahan bakar, pantai Rupat, istana siak dan Candi Muara Takus. pembuatan obyek dan membaca penanda scan dari kamera menggunakan teknik markerbase dan Markerless dengan objek Pelacakan metode 3D dan algoritma SIFT (Skala Fitur invarian Transform). Proses yang terkandung dalam deteksi obyek membaca gambar, mempertajam gambar dengan memanfaatkan High Pass Filter; membaca gambar dari metode SIFT bahwa proses akan menghasilkan deteksi titik. Hasil pengujian untuk melihat efek dari jarak antara smartphone kamera dengan spidol, jarak yang diperoleh sangat ideal untuk menampilkan objek 3D, sampai 40 cm. pengujian aplikasi ARRiauTouris mampu mendeteksi penanda dengan jarak dekat 10 cm dan jarak maksimum 67 cm, dan memperoleh waktu rata-rata untuk objek (mean) antara 0,80 detik menjadi 0,93 detik. Kata kunci: obyek wisata, augmented reality, 3D pelacakan objek, provinsi riau

scholarly journals Real-Time Occlusion Handling in Augmented Reality Based on an Object Tracking Approach

Sensors ◽  
2010 ◽  
Vol 10 (4) ◽  
pp. 2885-2900 ◽  
Author(s):  
Yuan Tian ◽  
Tao Guan ◽  
Cheng Wang
Keyword(s):  
Augmented Reality ◽  
Object Tracking ◽  
Real Time ◽  
Occlusion Handling

Real-time semantics based plane object tracking for augmented reality

2020 ◽  
Author(s):  
Shaowu Peng ◽  
Ju Chen ◽  
Honggang Guan ◽  
Ruojing Huang ◽  
Shichao Ou
Keyword(s):  
Augmented Reality ◽  
Object Tracking ◽  
Real Time ◽  
Time Semantics

scholarly journals A Robust Algorithm for Real Time Tracking With Optical Flow

2019 ◽  
Vol 9 (1) ◽  
pp. 887-892
Keyword(s):  
Optical Flow ◽  
Object Tracking ◽  
Real Time ◽  
Traffic Monitoring ◽  
Moving Object ◽  
Computational Time ◽  
Multiple Objects ◽  
Flow Information ◽  
Data Processor ◽  
Real Time Tracking

Object tracking is a troublesome undertaking and significant extent in data processor perception and image handling community. Some of the applications are protection surveillance, traffic monitoring on roads, offense detection and medical imaging. In this paper a recent technique for tracking of moving object is intended. Optical flow information authorizes us to know the displacement and speed of objects personate in a scene. Apply optical flow to the image gives flow vectors of the points to distinguishing the moving aspects. Optical flow is accomplished by Lucas canade algorithm. This algorithm is superior to other algorithms. The outcomes reveals that the intend algorithm is efficient and accurate object tracking method. This paper depicts a smoothing algorithm to track the moving object of both single and multiple objects in real time. The main issue of high computational time is greatly reduced in this proposed work

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