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Publications (10 of 36) Show all publications
Danelljan, M., Khan, F. S., Felsberg, M., Granström, K., Heintz, F., Rudol, P., . . . Doherty, P. (2015). A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems. In: Lourdes Agapito, Michael M. Bronstein and Carsten Rother (Ed.), Lourdes Agapito, Michael M. Bronstein and Carsten Rother (Ed.), COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I: . Paper presented at 13th European Conference on Computer Vision (ECCV) Switzerland, September 6-7 and 12 (pp. 223-237). Springer Publishing Company, 8925
Open this publication in new window or tab >>A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems
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2015 (English)In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I / [ed] Lourdes Agapito, Michael M. Bronstein and Carsten Rother, Springer Publishing Company, 2015, Vol. 8925, p. 223-237Conference paper, Published paper (Refereed)
Abstract [en]

Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.

In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios

Place, publisher, year, edition, pages
Springer Publishing Company, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 8925
Keywords
Visual tracking; Visual surveillance; Micro UAV; Active vision
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-115847 (URN)10.1007/978-3-319-16178-5_15 (DOI)000362493800015 ()978-3-319-16177-8 (ISBN)978-3-319-16178-5 (ISBN)
Conference
13th European Conference on Computer Vision (ECCV) Switzerland, September 6-7 and 12
Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2018-02-07Bibliographically approved
Granström, K., Natale, A., Braca, P., Ludeno, G. & Serafino, F. (2015). Gamma Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking Using X-Band Marine Radar Data. IEEE Transactions on Geoscience and Remote Sensing, 53(12), 6617-6631
Open this publication in new window or tab >>Gamma Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking Using X-Band Marine Radar Data
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2015 (English)In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 53, no 12, p. 6617-6631Article in journal (Refereed) Published
Abstract [en]

X-band marine radar systems represent a flexible and low-cost tool for the tracking of multiple targets in a given region of interest. Although suffering several sources of interference, e.g., the sea clutter, these systems can provide high-resolution measurements, both in space and time. Such features offer the opportunity to get accurate information not only about the target position/motion but also about the targets size. Accordingly, in this paper, we exploit emergent extended target tracking (ETT) methodologies in which the target state, typically position/velocity/acceleration, is augmented with the target length and width. In this paper, we propose an ETT procedure based on the popular probability hypothesis density filter, and in particular, we describe the extended target state through the gamma Gaussian inverse Wishart model. The comparative simplicity of the used models allows us to meet the real-time processing constraint required for the practical surveillance purposes. Real-world data from an experimental and operational campaign, collected during the recovery operations of the Costa Concordia wreckage in October 2013, are used to assess the performance of the proposed target tracking methodology. The full signal processing chain is implemented, and considerations of the experimental results are provided. Important nonideal effects, common to every marine radar, are observed and discussed in relation to the assumptions made for the tracking procedure.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2015
Keywords
Extended target tracking (ETT); gamma Gaussian inverse Wishart-probability hypothesis density (GGIW-PHD); multiple target tracking (MTT); probability hypothesis density (PHD); random finite sets (RFSs); real-world experimental results; X-band marine radar
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-121884 (URN)10.1109/TGRS.2015.2444794 (DOI)000361532400024 ()
Note

Funding Agencies|Italian Ministry of University and Research

Available from: 2015-10-13 Created: 2015-10-12 Last updated: 2017-12-01
Ardeshiri, T., Granström, K., Özkan, E. & Orguner, U. (2015). Greedy Reduction Algorithms for Mixtures of Exponential Family. IEEE Signal Processing Letters, 22(6), 676-680
Open this publication in new window or tab >>Greedy Reduction Algorithms for Mixtures of Exponential Family
2015 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 6, p. 676-680Article in journal (Refereed) Published
Abstract [en]

In this letter, we propose a general framework for greedy reduction of mixture densities of exponential family. The performances of the generalized algorithms are illustrated both on an artificial example where randomly generated mixture densities are reduced and on a target tracking scenario where the reduction is carried out in the recursion of a Gaussian inverse Wishart probability hypothesis density (PHD) filter.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015
Keywords
Exponential family; extended target; integral square error; Kullback-Leibler divergence; mixture density; mixture reduction; target tracking
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-112990 (URN)10.1109/LSP.2014.2367154 (DOI)000345236400005 ()
Note

Funding Agencies|Swedish research council (VR) under ETT [621-2010-4301]; SSF, project CUAS

Available from: 2015-01-12 Created: 2015-01-08 Last updated: 2017-12-05
Granström, K., Reuter, S., Meissner, D. & Scheel, A. (2014). A multiple model PHD approach to tracking of cars under an assumed rectangular shape. In: 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION): . Paper presented at 17th International Conference on Information Fusion (FUSION). IEEE
Open this publication in new window or tab >>A multiple model PHD approach to tracking of cars under an assumed rectangular shape
2014 (English)In: 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), IEEE , 2014Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an extended target tracking method for tracking cars in urban traffic using data from laser range sensors. Results are presented for three real world datasets that contain multiple cars, occlusions, and maneuver changes. The cars shape is approximated by a rectangle, and single track steering models are used for the target kinematics. A multiple model approach is taken for both the dynamics modeling and the measurement modeling. A comparison to ground truth shows that the estimation errors are generally very small: on average the absolute error is less than half a degree for the heading. Multiple cars are handled using a multiple model PHD filter, where a variable probability of detection is integrated to enable tracking of occluded cars.

Place, publisher, year, edition, pages
IEEE, 2014
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-123343 (URN)000363896100022 ()
Conference
17th International Conference on Information Fusion (FUSION)
Available from: 2015-12-11 Created: 2015-12-11 Last updated: 2015-12-11
Granström, K. & Orguner, U. (2014). New Prediction for Extended Targets With Random Matrices. IEEE Transactions on Aerospace and Electronic Systems, 50(2), 1577-1589
Open this publication in new window or tab >>New Prediction for Extended Targets With Random Matrices
2014 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 50, no 2, p. 1577-1589Article in journal (Refereed) Published
Abstract [en]

This paper presents a new prediction update for extended targets whose extensions are modeled as random matrices. The prediction is based on several minimizations of the Kullback-Leibler divergence (KL-div) and allows for a kinematic state dependent transformation of the target extension. The results show that the extension prediction is a significant improvement over the previous work carried out on the topic.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2014
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-112656 (URN)10.1109/TAES.2014.120211 (DOI)000344364500059 ()
Note

Funding Agencies|Linnaeus research environment CADICS - Swedish Research Council; frame project grant Extended Target Tracking - Swedish Research Council [621-2010-4301]; Collaborative Unmanned Aircraft Systems (CUAS) - Swedish Foundation for Strategic Research (SSF)

Available from: 2014-12-05 Created: 2014-12-05 Last updated: 2017-12-05
Granström, K., Natale, A., Braca, P., Ludeno, G. & Serafino, F. (2014). PHD Extended Target Tracking Using an Incoherent X-band Radar: Preliminary Real-World Experimental Results. In: 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION): . Paper presented at 17th International Conference on Information Fusion (FUSION). IEEE
Open this publication in new window or tab >>PHD Extended Target Tracking Using an Incoherent X-band Radar: Preliminary Real-World Experimental Results
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2014 (English)In: 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), IEEE , 2014Conference paper, Published paper (Refereed)
Abstract [en]

X-band radar systems represent a flexible and low-cost tool for ship detection and tracking. These systems suffer the interference of the sea-clutter but at the same time they can provide high measurement resolutions, both in space and time. Such features offer the opportunity to get accurate information about the targets state and shape. Accordingly, here we exploit an extended target tracking methodology based on the popular Probability Hypothesis Density to get information about the targets observed in an actual X-band radar dataset. For each target track we estimate the targets position, velocity and acceleration, as well as its size and the expected number of radar returns.

Place, publisher, year, edition, pages
IEEE, 2014
Keywords
Multiple target tracking; GGIW-PHD; X-band radar; extended targets; real-world experimental results
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-123344 (URN)000363896100303 ()
Conference
17th International Conference on Information Fusion (FUSION)
Available from: 2015-12-11 Created: 2015-12-11 Last updated: 2015-12-11
Granström, K., Lundquist, C., Gustafsson, F. & Orguner, U. (2014). Random Set Methods: Estimation of Multiple Extended Objects. IEEE robotics & automation magazine, 21(2), 73-82
Open this publication in new window or tab >>Random Set Methods: Estimation of Multiple Extended Objects
2014 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 21, no 2, p. 73-82Article in journal (Refereed) Published
Abstract [en]

Random set based methods have provided a rigorous Bayesian framework and have been used extensively in the last decade for point object estimation. In this paper, we emphasize that the same methodology offers an equally powerful approach to estimation of so called extended objects, i.e., objects that result in multiple detections on the sensor side. Building upon the analogy between Bayesian state estimation of a single object and random finite set estimation for multiple objects, we give a tutorial on random set methods with an emphasis on multiple extended object estimation. The capabilities are illustrated on a simple yet insightful real life example with laser range data containing several occlusions.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2014
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-105530 (URN)10.1109/MRA.2013.2283185 (DOI)000337124700012 ()
Projects
CADICSCUAS
Funder
Linnaeus research environment CADICS
Available from: 2014-03-26 Created: 2014-03-26 Last updated: 2017-12-05Bibliographically approved
Scheel, A., Granström, K., Meissner, D., Reuter, S. & Dietmayer, K. (2014). Tracking and data segmentation using a GGIW filter with mixture clustering. In: FUSION 2014 - 17th International Conference on Information Fusion: . Paper presented at 17th International Conference on Information Fusion, FUSION 2014. Institute of Electrical and Electronics Engineers Inc. ( 6916137)
Open this publication in new window or tab >>Tracking and data segmentation using a GGIW filter with mixture clustering
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2014 (English)In: FUSION 2014 - 17th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers Inc. , 2014, no 6916137Conference paper, Published paper (Refereed)
Abstract [en]

Common data preprocessing routines often introduce considerable flaws in laser-based tracking of extended objects. As an alternative, extended target tracking methods, such as the Gamma-Gaussian-Inverse Wishart (GGIW) probability hypothesis density (PHD) filter, work directly on raw data. In this paper, the GGIW-PHD filter is applied to real world traffic scenarios. To cope with the large amount of data, a mixture clustering approach which reduces the combinatorial complexity and computation time is proposed. The effective segmentation of raw measurements with respect to spatial distribution and motion is demonstrated and evaluated on two different applications: pedestrian tracking from a vehicle and intersection surveillance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2014
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-116787 (URN)000363896100168 ()2-s2.0-84910686579 (Scopus ID)9788490123553 (ISBN)
Conference
17th International Conference on Information Fusion, FUSION 2014
Available from: 2015-04-09 Created: 2015-04-02 Last updated: 2015-12-14
Lundquist, C., Granström, K. & Orguner, U. (2013). An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation. IEEE Journal on Selected Topics in Signal Processing, 7(3), 472-483
Open this publication in new window or tab >>An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation
2013 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 7, no 3, p. 472-483Article in journal (Refereed) Published
Abstract [en]

This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has been derived by Mahler, and different implementations have been proposed recently. To achieve better estimation performance this work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers. A gamma Gaussian inverse Wishart mixture implementation, which is capable of estimating the target extents and measurement rates as well as the kinematic state of the target, is proposed, and it is compared to its PHD counterpart in a simulation study. The results clearly show that the CPHD filter has a more robust cardinality estimate leading to smaller OSPA errors, which confirms that the extended target CPHD filter inherits the properties of its point target counterpart.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013
Keywords
Cardinalized, CPHD, Extended targets, Inverse Wishart, Multiple target tracking, Probability hypothesis density, PHD, Random matrices, Random sets
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-94596 (URN)10.1109/JSTSP.2013.2245632 (DOI)000319275500010 ()
Projects
CADICSCUAS
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research
Note

Funding Agencies|Swedish Research Council under the Linnaeus Center (CADICS)||Swedish Research Council|621-2010-4301|Swedish Foundation for Strategic Research||

Available from: 2013-06-27 Created: 2013-06-27 Last updated: 2017-12-06Bibliographically approved
Lundquist, C., Skoglund, M., Granström, K. & Glad, T. (2013). Insights from Implementing a System for Peer-Review. IEEE Transactions on Education, 56(3), 261-267
Open this publication in new window or tab >>Insights from Implementing a System for Peer-Review
2013 (English)In: IEEE Transactions on Education, ISSN 0018-9359, E-ISSN 1557-9638, Vol. 56, no 3, p. 261-267Article in journal (Refereed) Published
Abstract [en]

Courses at the Master’s level in automatic control and signal processing cover mathematical theories and algorithms for control, estimation, and filtering. However, giving students practical experience in how to use these algorithms is also an important part of these courses. A goal is that the students should not only be able to understand and derive these algorithms, but also be able to apply them to real-life technical problems. The latter is achieved by assigning more time to the laboratory tutorials and designing them in such a way that the exercises are open for interpretation; an example of this would be giving the students more freedom to decide how to acquire the data needed to solve the given exercises.The students are asked to hand in a laboratory report in which they describe how they solved the exercises. This paper presents a double-blind peer-review process for laboratory reports, introduced at the Department of Electrical Engineering, Linköping University, Sweden. A survey was administered to students, and the results are summarized in this paper. Also discussed are the teachers’ experiences of peer review and of how students perform later in their education in writing their Master’s theses.

Keywords
Critical thinking, laboratory work, peer assessment, student learning, peer review, student self-assessment, team-based projects
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-96893 (URN)10.1109/TE.2012.2211876 (DOI)000322657200002 ()
Available from: 2013-08-28 Created: 2013-08-28 Last updated: 2017-12-06
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3450-988X

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