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Shape Based Recognition – Cognitive Vision Systems in Traffic Safety Applications
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
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: urn:nbn:se:liu:diva-71664ISBN: 978-91-7393-074-1 (print)OAI: oai:DiVA.org:liu-71664DiVA: diva2:452207
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
List of papers
1. Torchlight Navigation
Open this publication in new window or tab >>Torchlight Navigation
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2010 (English)In: Proceedings of the 20th International Conferenceon Pattern Recognition, 2010, 302-306 p.Conference paper, Published paper (Refereed)
Abstract [en]

A common computer vision task is navigation and mapping. Many indoor navigation tasks require depth knowledge of flat, unstructured surfaces (walls, floor, ceiling). With passive illumination only, this is an ill-posed problem. Inspired by small children using a torchlight, we use a spotlight for active illumination. Using our torchlight approach, depth and orientation estimation of unstructured, flat surfaces boils down to estimation of ellipse parameters. The extraction of ellipses is very robust and requires little computational effort.

Series
International Conference on Pattern Recognition, ISSN 1051-4651
Keyword
Torchlight, Pose estimation, Active illumination, Plane estimation, Ellipses
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-60597 (URN)10.1109/ICPR.2010.83 (DOI)978-1-4244-7542-1 (ISBN)978-0-7695-4109-9 (ISBN)
Conference
20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August, 2010
Projects
DIPLECSGARNICSELLIITCADICS
Funder
Swedish Foundation for Strategic Research
Available from: 2010-10-20 Created: 2010-10-20 Last updated: 2016-05-04Bibliographically approved
2. Bicycle Tracking Using Ellipse Extraction
Open this publication in new window or tab >>Bicycle Tracking Using Ellipse Extraction
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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
Keyword
Tracking, Particle Filter, Computer Vision, Ellipse Extraction, Bicycle
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-69672 (URN)978-1-4577-0267-9 (ISBN)
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
3. Correlating Fourier descriptors of local patches for road sign recognition
Open this publication in new window or tab >>Correlating Fourier descriptors of local patches for road sign recognition
2011 (English)In: IET Computer Vision, ISSN 1751-9632, E-ISSN 1751-9640, Vol. 5, no 4, 244-254 p.Article in journal (Refereed) Published
Abstract [en]

The Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain. FDs are most commonly used to compare object silhouettes and object contours; the authors instead use this well-established machinery to describe local regions to be used in an object-recognition framework. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples. The authors show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. The authors compare correlation-based matching against affine-invariant Fourier descriptors (AFDs) and WARP-matched FDs and demonstrate that correlation-based approach outperforms AFDs and WARP on real data. As a practical application the authors demonstrate the proposed correlation-based matching on a road sign recognition task.

Place, publisher, year, edition, pages
IET, 2011
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-65621 (URN)10.1049/iet-cvi.2010.0040 (DOI)000291385900007 ()
Projects
DIPLECS, GARNICS, ELLIIT
Note
This paper is a postprint of a paper submitted to and accepted for publication in IET Computer Vision and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library Fredrik Larsson, Michael Felsberg and Per-Erik Forssen, Correlating Fourier descriptors of local patches for road sign recognition, 2011, IET Computer Vision, (5), 4, 244-254. http://dx.doi.org/10.1049/iet-cvi.2010.0040 Copyright: Iet http://www.theiet.org/ Available from: 2011-02-14 Created: 2011-02-14 Last updated: 2017-12-11Bibliographically approved
4. Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition
Open this publication in new window or tab >>Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition
2011 (English)In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings / [ed] Anders Heyden, Fredrik Kahl, Springer Berlin/Heidelberg, 2011, 238-249 p.Conference paper, Published paper (Refereed)
Abstract [en]

Traffic sign recognition is important for the development of driver assistance systems and fully autonomous vehicles. Even though GPS navigator systems works well for most of the time, there will always be situations when they fail. In these cases, robust vision based systems are required. Traffic signs are designed to have distinct colored fields separated by sharp boundaries. We propose to use locally segmented contours combined with an implicit star-shaped object model as prototypes for the different sign classes. The contours are described by Fourier descriptors. Matching of a query image to the sign prototype database is done by exhaustive search. This is done efficiently by using the correlation based matching scheme for Fourier descriptors and a fast cascaded matching scheme for enforcing the spatial requirements. We demonstrated on a publicly available database state of the art performance.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2011
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 6688
Keyword
Traffic sign recognition – Fourier descriptors – spatial models – traffic sign dataset
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-69521 (URN)10.1007/978-3-642-21227-7_23 (DOI)000308543900023 ()978-3-642-21226-0 (ISBN)978-3-642-21227-7 (ISBN)
Conference
17th Scandinavian Conference on Image Analysis (SCIA), Ystad, Sweden, May 23-27, 2011
Note

Original Publication: Fredrik Larsson and Michael Felsberg, Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition, SCIA konferens, 23-27 May 2011, Ystad Sweden, 2011, Lecture Notes in Computer Science, Image Analysis, 238-249. http://dx.doi.org/10.1007/978-3-642-21227-7_23 Copyright: Springer

Available from: 2011-06-30 Created: 2011-06-30 Last updated: 2016-05-04Bibliographically approved
5. Learning Higher-Order Markov Models for ObjectTracking in Image Sequences
Open this publication in new window or tab >>Learning Higher-Order Markov Models for ObjectTracking in Image Sequences
2009 (English)In: Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II, Berlin, Heidelberg: Springer-Verlag , 2009, 184-195 p.Conference paper, Published paper (Refereed)
Abstract [en]

This work presents a novel object tracking approach, where the motion model is learned from sets of frame-wise detections with unknown associations. We employ a higher-order Markov model on position space instead of a first-order Markov model on a high-dimensional state-space of object dynamics. Compared to the latter, our approach allows the use of marginal rather than joint distributions, which results in a significant reduction of computation complexity. Densities are represented using a grid-based approach, where the rectangular windows are replaced with estimated smooth Parzen windows sampled at the grid points. This method performs as accurately as particle filter methods with the additional advantage that the prediction and update steps can be learned from empirical data. Our method is compared against standard techniques on image sequences obtained from an RC car following scenario. We show that our approach performs best in most of the sequences. Other potential applications are surveillance from cheap or uncalibrated cameras and image sequence analysis.

Place, publisher, year, edition, pages
Berlin, Heidelberg: Springer-Verlag, 2009
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 5876
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-50495 (URN)10.1007/978-3-642-10520-3_17 (DOI)000279247100017 ()978-3-642-10519-7 (ISBN)
Conference
The 5th International Symposium on Advances in Visual Computing (ISVC), Las Vegas, USA, December
Projects
DIPLECS
Available from: 2009-10-12 Created: 2009-10-12 Last updated: 2016-05-04Bibliographically approved
6. Simultaneously learning to recognize and control a low-cost robotic arm
Open this publication in new window or tab >>Simultaneously learning to recognize and control a low-cost robotic arm
2009 (English)In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 27, no 11, 1729-1739 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a visual servoing method based on a learned mapping between feature space and control space. Using a suitable recognition algorithm, we present and evaluate a complete method that simultaneously learns the appearance and control of a low-cost robotic arm. The recognition part is trained using an action precedes perception approach. The novelty of this paper, apart from the visual servoing method per se, is the combination of visual servoing with gripper recognition. We show that we can achieve high precision positioning without knowing in advance what the robotic arm looks like or how it is controlled.

Keyword
Gripper recognition; Jacobian estimation; LWPR; Visual servoing
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-21195 (URN)10.1016/j.imavis.2009.04.003 (DOI)
Note
Original Publication: Fredrik Larsson, Erik Jonsson and Michael Felsberg, Simultaneously learning to recognize and control a low-cost robotic arm, 2009, Image and Vision Computing, (27), 11, 1729-1739. http://dx.doi.org/10.1016/j.imavis.2009.04.003 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/ Available from: 2009-09-30 Created: 2009-09-30 Last updated: 2017-12-13Bibliographically approved

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