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Bicycle Tracking Using Ellipse Extraction
Linköping University, Department of Electrical Engineering, Automatic Control. 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, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
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2011 (English)In: Proceedings of the 14thInternational Conference on Information Fusion, 2011, IEEE , 2011, 1-8 p.Conference paper, Published paper (Refereed)
Abstract [en]

A new approach to track bicycles from imagery sensor data is proposed. It is based on detecting ellipsoids in the images, and treat these pair-wise using a dynamic bicycle model. One important application area is in automotive collision avoidance systems, where no dedicated systems for bicyclists yet exist and where very few theoretical studies have been published.

Possible conflicts can be predicted from the position and velocity state in the model, but also from the steering wheel articulation and roll angle that indicate yaw changes before the velocity vector changes. An algorithm is proposed which consists of an ellipsoid detection and estimation algorithm and a particle filter.

A simulation study of three critical single target scenarios is presented, and the algorithm is shown to produce excellent state estimates. An experiment using a stationary camera and the particle filter for state estimation is performed and has shown encouraging results.

Place, publisher, year, edition, pages
IEEE , 2011. 1-8 p.
Keyword [en]
Tracking, Particle Filter, Computer Vision, Ellipse Extraction, Bicycle
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-69672ISBN: 978-1-4577-0267-9 (print)OAI: oai:DiVA.org:liu-69672DiVA: diva2:430910
Conference
The 14th International Conference on Information Fusion, 5-8 July 2011, Chicago, IL, USA
Available from: 2011-07-13 Created: 2011-07-13 Last updated: 2016-05-04Bibliographically approved
In thesis
1. Shape Based Recognition – Cognitive Vision Systems in Traffic Safety Applications
Open this publication in new window or tab >>Shape Based Recognition – Cognitive Vision Systems in Traffic Safety Applications
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Traffic accidents are globally the number one cause of death for people 15-29 years old and is among the top three causes for all age groups 5-44 years. Much of the work within this thesis has been carried out in projects aiming for (cognitive) driver assistance systems and hopefully represents a step towards improving traffic safety.

The main contributions are within the area of Computer Vision, and more specifically, within the areas of shape matching, Bayesian tracking, and visual servoing with the main focus being on shape matching and applications thereof. The different methods have been demonstrated in traffic safety applications, such as  bicycle tracking, car tracking, and traffic sign recognition, as well as for pose estimation and robot control.

One of the core contributions is a new method for recognizing closed contours, based on complex correlation of Fourier descriptors. It is shown that keeping the phase of Fourier descriptors is important. Neglecting the phase can result in perfect matches between intrinsically different shapes. Another benefit of keeping the phase is that rotation covariant or invariant matching is achieved in the same way. The only difference is to either consider the magnitude, for rotation invariant matching, or just the real value, for rotation covariant matching, of the complex valued correlation.

The shape matching method has further been used in combination with an implicit star-shaped object model for traffic sign recognition. The presented method works fully automatically on query images with no need for regions-of-interests. It is shown that the presented method performs well for traffic signs that contain multiple distinct contours, while some improvement still is needed for signs defined by a single contour. The presented methodology is general enough to be used for arbitrary objects, as long as they can be defined by a number of regions.

Another contribution has been the extension of a framework for learning based Bayesian tracking called channel based tracking. Compared to earlier work, the multi-dimensional case has been reformulated in a sound probabilistic way and the learning algorithm itself has been extended. The framework is evaluated in car tracking scenarios and is shown to give competitive tracking performance, compared to standard approaches, but with the advantage of being fully learnable.

The last contribution has been in the field of (cognitive) robot control. The presented method achieves sufficient accuracy for simple assembly tasks by combining autonomous recognition with visual servoing, based on a learned mapping between percepts and actions. The method demonstrates that limitations of inexpensive hardware, such as web cameras and low-cost robotic arms, can be overcome using powerful algorithms.

All in all, the methods developed and presented in this thesis can all be used for different components in a system guided by visual information, and hopefully represents a step towards improving traffic safety.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 49 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1395
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-71664 (URN)978-91-7393-074-1 (ISBN)
Public defence
2011-11-25, Vallfarten, hus Vallfarten, Campus Valla, Linköpings universitet, Linköping, 09:15 (English)
Opponent
Supervisors
Available from: 2011-10-28 Created: 2011-10-28 Last updated: 2016-05-04Bibliographically approved

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Ardeshiri, TohidLarsson, FredrikGustafsson, FredrikSchön, Thomas B.Felsberg, Michael

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