Fusing 2D and 3D Clues for 3D Tracking Using Visual and Range Data

Date

2013-07-12

Author

Gedik, O. Serdar
Alatan, Abdullah Aydın

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

159
views

0
downloads

3D tracking of rigid objects is required in many applications, such as robotics or augmented reality (AR). The availability of accurate pose estimates increases reliability in robotic applications and decreases jitter in AR scenarios. Pure vision-based 3D trackers require either manual initializations or offline training stages, whereas trackers relying on pure depth sensors are not suitable for AR applications. In this paper, an automated 3D tracking algorithm, which is based on fusion of vision and depth sensors via Extended Kalman Filter (EKF), which inherits a novel observation weighting method, is proposed. Moreover, novel feature selection and tracking schemes based on intensity and shape index map (SIM) data of 3D point cloud, increases 2D and 3D tracking performance significantly. The proposed method requires neither manual initialization of pose nor offline training, while enabling highly accurate 3D tracking. The accuracy of the proposed method is tested against a number of conventional techniques and superior performance is observed.

Subject Keywords

3D tracking, Sensor fusion, EKF

URI

https://hdl.handle.net/11511/55678

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

UNCERTAINTY MODELING FOR EFFICIENT VISUAL ODOMETRY VIA INERTIAL SENSORS ON MOBILE DEVICES AKSOY, Yagiz; Alatan, Abdullah Aydın (2014-10-30) Most of the mobile applications require efficient and precise computation of the device pose, and almost every mobile device has inertial sensors already equipped together with a camera. This fact makes sensor fusion quite attractive for increasing efficiency during pose tracking. However, the state-of-the-art fusion algorithms have a major shortcoming: lack of well-defined uncertainty introduced to the system during the prediction stage of the fusion filters. Such a drawback results in determining covarian...
3-D Rigid Body Tracking Using Vision and Depth Sensors Gedik, O. Serdar; Alatan, Abdullah Aydın (Institute of Electrical and Electronics Engineers (IEEE), 2013-10-01) In robotics and augmented reality applications, model-based 3-D tracking of rigid objects is generally required. With the help of accurate pose estimates, it is required to increase reliability and decrease jitter in total. Among many solutions of pose estimation in the literature, pure vision-based 3-D trackers require either manual initializations or offline training stages. On the other hand, trackers relying on pure depth sensors are not suitable for AR applications. An automated 3-D tracking algorithm,...
Application and Modeling of a Magnetic WSN for Target Localization Baghaee, Sajjad; GÜRBÜZ, SEVGİ ZÜBEYDE; Uysal, Elif (2013-04-12) The aim of this study is modeling ferromagnetic targets for localization and identification of such objects by a wireless sensor network (WSN). MICAz motes were used for setting up a wireless sensor network utilizing a centralized tree-based system. The detection and tracking of ferromagnetic objects is an important application of WSNs. This research focuses on analyzing the sensing limitations of magnetic sensors via tests conducted on small-scale targets which are moving within a 30 cm radius around the s...
Accurate 3D tracking using visual and depth data Gedik, Osman Serdar; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2014) 3D tracking of objects is essential in many applications, such as robotics and aug- mented reality (AR), and availability of accurate pose estimates increases reliability in robotic applications whereas decreases jitter in AR scenarios. As a result of the recent advances in the sensor technology, it is possible to capture synchronous high frame rate RGB and depth data. With this motivation, an automated and high accurate 3D tracking algorithm based on simultaneous utilization of visual and depth sensors is ...
2D-3D feature association via projective transform invariants for model-based 3D pose estimation Gedik, O. Serdar; Alatan, Abdullah Aydın (2012-01-26) The three dimensional (3D) tracking of rigid objects is required in many applications, such as 3D television (3DTV) and augmented reality. Accurate and robust pose estimates enable improved structure reconstructions for 3DTV and reduce jitter in augmented reality scenarios. On the other hand, reliable 2D-3D feature association is one of the most crucial requirements for obtaining high quality 3D pose estimates. In this paper, a 2D-3D registration method, which is based on projective transform invariants, is...

Citation Formats

O. S. Gedik and A. A. Alatan, “Fusing 2D and 3D Clues for 3D Tracking Using Visual and Range Data,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55678.