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KLT Tracking Implementation on the GPU
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6096-3648
2007 (English)In: Proceedings SSBA 2007 / [ed] Magnus Borga, Anders Brun and Michael Felsberg;, 2007Conference paper, Oral presentation only (Other academic)
Abstract [en]

The GPU is the main processing unit on a graphics card. A modern GPU typically provides more than ten times the computational power of an ordinary PC processor. This is a result of the high demands for speed and image quality in computer games. This paper investigates the possibility of exploiting this computational power for tracking points in image sequences. Tracking points is used in many computer vision tasks, such as tracking moving objects, structure from motion, face tracking etc. The algorithm was successfully implemented on the GPU and a large speed up was achieved.

Place, publisher, year, edition, pages
2007.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-21602OAI: oai:DiVA.org:liu-21602DiVA: diva2:241567
Conference
SSBA, Swedish Symposium in Image Analysis 2007, 14-15 March, Linköping, Sweden
Available from: 2009-10-05 Created: 2009-10-05 Last updated: 2016-05-04
In thesis
1. Pose Estimation and Structure Analysisof Image Sequences
Open this publication in new window or tab >>Pose Estimation and Structure Analysisof Image Sequences
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Autonomous navigation for ground vehicles has many challenges. Autonomous systems must be able to self-localise, avoid obstacles and determine navigable surfaces. This thesis studies several aspects of autonomous navigation with a particular emphasis on vision, motivated by it being a primary component for navigation in many high-level biological organisms.  The key problem of self-localisation or pose estimation can be solved through analysis of the changes in appearance of rigid objects observed from different view points. We therefore describe a system for structure and motion estimation for real-time navigation and obstacle avoidance. With the explicit assumption of a calibrated camera, we have studied several schemes for increasing accuracy and speed of the estimation.The basis of most structure and motion pose estimation algorithms is a good point tracker. However point tracking is computationally expensive and can occupy a large portion of the CPU resources. In thisthesis we show how a point tracker can be implemented efficiently on the graphics processor, which results in faster tracking of points and the CPU being available to carry out additional processing tasks.In addition we propose a novel view interpolation approach, that can be used effectively for pose estimation given previously seen views. In this way, a vehicle will be able to estimate its location by interpolating previously seen data.Navigation and obstacle avoidance may be carried out efficiently using structure and motion, but only whitin a limited range from the camera. In order to increase this effective range, additional information needs to be incorporated, more specifically the location of objects in the image. For this, we propose a real-time object recognition method, which uses P-channel matching, which may be used for improving navigation accuracy at distances where structure estimation is unreliable.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. 28 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1418
Keyword
KLT, GPU, structure from motion, stereo, pose estimation
National Category
Engineering and Technology Computer Vision and Robotics (Autonomous Systems) Signal Processing
Identifiers
urn:nbn:se:liu:diva-58706 (URN)LiU-TEK-LIC-2009:26 (Local ID)978-91-7393-516-6 (ISBN)LiU-TEK-LIC-2009:26 (Archive number)LiU-TEK-LIC-2009:26 (OAI)
Opponent
Supervisors
Projects
Diplecs
Available from: 2011-01-25 Created: 2010-08-23 Last updated: 2016-05-04Bibliographically approved
2. Motion and Structure Estimation From Video
Open this publication in new window or tab >>Motion and Structure Estimation From Video
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Digital camera equipped cell phones were introduced in Japan in 2001, they quickly became popular and by 2003 outsold the entire stand-alone digital camera market. In 2010 sales passed one billion units and the market is still growing. Another trend is the rising popularity of smartphones which has led to a rapid development of the processing power on a phone, and many units sold today bear close resemblance to a personal computer. The combination of a powerful processor and a camera which is easily carried in your pocket, opens up a large eld of interesting computer vision applications.

The core contribution of this thesis is the development of methods that allow an imaging device such as the cell phone camera to estimates its own motion and to capture the observed scene structure. One of the main focuses of this thesis is real-time performance, where a real-time constraint does not only result in shorter processing times, but also allows for user interaction.

In computer vision, structure from motion refers to the process of estimating camera motion and 3D structure by exploring the motion in the image plane caused by the moving camera. This thesis presents several methods for estimating camera motion. Given the assumption that a set of images has known camera poses associated to them, we train a system to solve the camera pose very fast for a new image. For the cases where no a priory information is available a fast minimal case solver is developed. The solver uses ve points in two camera views to estimate the cameras relative position and orientation. This type of minimal case solver is usually used within a RANSAC framework. In order to increase accuracy and performance a renement to the random sampling strategy of RANSAC is proposed. It is shown that the new scheme doubles the performance for the ve point solver used on video data. For larger systems of cameras a new Bundle Adjustment method is developed which are able to handle video from cell phones.

Demands for reduction in size, power consumption and price has led to a redesign of the image sensor. As a consequence the sensors have changed from a global shutter to a rolling shutter, where a rolling shutter image is acquired row by row. Classical structure from motion methods are modeled on the assumption of a global shutter and a rolling shutter can severely degrade their performance. One of the main contributions of this thesis is a new Bundle Adjustment method for cameras with a rolling shutter. The method accurately models the camera motion during image exposure with an interpolation scheme for both position and orientation.

The developed methods are not restricted to cellphones only, but is rather applicable to any type of mobile platform that is equipped with cameras, such as a autonomous car or a robot. The domestic robot comes in many  avors, everything from vacuum cleaners to service and pet robots. A robot equipped with a camera that is capable of estimating its own motion while sensing its environment, like the human eye, can provide an eective means of navigation for the robot. Many of the presented methods are well suited of robots, where low latency and real-time constraints are crucial in order to allow them to interact with their environment.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 42 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1449
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-76904 (URN)978-91-7519-892-7 (ISBN)
Public defence
2012-05-16, Visionen, hus B, Campus Valla, Linköpings Universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Projects
Virtual Photo Set (VPS)
Available from: 2012-04-24 Created: 2012-04-24 Last updated: 2016-05-04Bibliographically approved

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Hedborg, JohanSkoglund, JohanFelsberg, Michael

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