Mitarbeiter

PD Dr.-Ing. Marco Huber

Lebenslauf

Marco Huber ist Privatdozent für Informatik am Lehrstuhl für Interaktive Echtzeitsysteme und Senior Consultant bei der USU Software AG . Von 2009 bis 2011 leitete er die Forschungsgruppe „Variable Bildgewinnung und -verarbeitung  " (VBV) des Lehrstuhls für Interaktive Echtzeitsysteme und eine gleichnamige Arbeitsgruppe am Fraunhofer IOSB  . Im Anschluss war er bis 2015 als Senior Researcher bei AGT International in Darmstadt tätig. Marco Huber studierte von 2000 bis 2006 Informatik an der Universität Karlsruhe (TH) und promovierte 2009 am Lehrstuhl für Intelligente Sensor-Aktor-Systeme  (ISAS), Universität Karlsruhe (TH), bei Prof. Uwe Hanebeck mit dem Thema „Probabilistic Framework for Sensor Management". Die Promotion entstand im Rahmen des DFG-Graduiertenkollegs 1194 „Selbstorganisierende Sensor-Aktor-Netzwerke  “. Im Januar 2015 beendete er erfolgreich seine Habilitation zum Thema „Nonlinear Gaussian Filtering – Theory, Algorithms, and Applications“.

Veröffentlichungen


2019
Gaussian process based dynamic facial emotion tracking.
Dunau, P.; Huber, M. F.; Beyerer, J.
2019. 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019; Howards Plaza HotelTaipei; Taiwan; 6 May 2019 through 9 May 2019, 248–253, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICPHYS.2019.8780338
Comparison of angle and size features with deep learning for emotion recognition.
Dunau, P.; Huber, M. F.; Beyerer, J.
2019. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings. Ed.: R. Vera-Rodriguez, 602–610, Springer. doi:10.1007/978-3-030-13469-3_70
Reduced feature set for emotion recognition based on angle and size information.
Dunau, P.; Bonny, M.; Huber, M. F.; Beyerer, J.
2019. 15th International Conference on Intelligent Autonomous Systems, IAS 2018; Baden-Baden; Germany; 11 June 2018 through 15 June 2018. Ed.: R. Dillmann, 585–596, Springer. doi:10.1007/978-3-030-01370-7_46
2018
Guest Editorial Special Section on Multisensor Fusion and Integration for Intelligent Systems (Editorial).
Hanebeck, U.; Baum, M.; Huber, M. F.
2018. IEEE transactions on industrial informatics, 14 (3), 1124–1126. doi:10.1109/TII.2018.2797956
Retrodiction of Data Association Probabilities via Convex Optimization.
Özgen, S.; Huber, M. F.; Rosenthal, F.; Mayer, J.; Noack, B.; Hanebeck, U. D.
2018. 2018 21st International Conference on Information Fusion (FUSION), 10-13 July, 2018, Cambridge, United Kingdom, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/ICIF.2018.8455857
2015
Paper I. Robust Filtering and Smoothing with Gaussian Processes. Edited version of the paper: M. P. Deisenroth, R. D. Turner, M. F. Huber, U. D. Hanebeck, and C. E. Rasmussen. Robust Filtering and Smoothing with Gaussian Processes. In IEEE Transactions on Automatic Control, vol. 57, no. 7, pages 1865-1871, July 2012.
Deisenroth, M. P.; Turner, R. D.; Huber, M. F.; Rasmussen, C. E.; Hanebeck, U. D.
2015. Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications. Ed.: M. Huber, 402–424, Karlsruher Institut für Technologie (KIT)
Paper E. (Semi-)Analytic Gaussian Mixture Filter. Edited version of the paper: M. F. Huber, F. Beutler, and U. D. Hanebeck. (Semi-)Analytic Gaussian Mixture Filter. In Proceedings of the 18th IFACWorld Congress, pages 10014-10020,Milano, Italy, August 2011.
Huber, M. F.; Beutler, F.; Hanebeck, U. D.
2015. Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications. Ed.: M. Huber, 310–331, Karlsruher Institut für Technologie (KIT)
Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications. Habilitation.
Huber, M.
2015. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000046060
2014
Low Resolution Person Detection with a Moving Thermal Infrared Camera by Hot Spot Classification.
Teutsch, M.; Mueller, T.; Huber, M.; Beyerer, J.
2014. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’14), Columbus, Ohio/USA, June 23-28, 2014, 209–216, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CVPRW.2014.40
2013
Gaussian filtering for polynomial systems based on moment homotopy.
Huber, M. F.; Hanebeck, U. D.
2013. 16th International Conference on Information Fusion (FUSION’13), Istanbul, Turkey, July 9-12, 2013, 1080–1087, Institute of Electrical and Electronics Engineers (IEEE)
2012
Robust Filtering and Smoothing with Gaussian Processes.
Deisenroth, M. P.; Darby Turner, R.; Huber, M. F.; Hanebeck, U. D.; Rasmussen, C. E.
2012
Bayesian active object recognition via Gaussian process regression.
Huber, M.; Dencker, T.; Roschani, M.; Beyerer, J.
2012. 2012 15th International Conference on Information Fusion (FUSION), 1718–1725, IEEE Computer Society
Robust Filtering and Smoothing with Gaussian Processes.
Deisenroth, M. P.; Darby Turner, R.; Huber, M. F.; Hanebeck, U. D.; Rasmussen, C. E.
2012. IEEE Transactions on Automatic Control, 57 (7), 1865–1871. doi:10.1109/TAC.2011.2179426
2011
(Semi-)Analytic Gaussian Mixture Filter.
Huber, M. F.; Beutler, F.; Hanebeck, U. D.
2011. Proceedings of the 18th IFAC World Congress (IFAC 2011), Milan, Italy, August 28 - September 2, 2011. Pt. 1. Ed.: S. Bittanti, 1014–1020, Institute of Electrical and Electronics Engineers (IEEE). doi:10.3182/20110828-6-IT-1002.03359
Superficial Gaussian Mixture Reduction.
Huber, M. F.; Krauthausen, P.; Hanebeck, U. D.
2011. Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2011), Berlin, Germany, 4. - 7.10.2011
Semi-Analytic Gaussian Assumed Density Filter.
Huber, M. F.; Beutler, F.; Hanebeck, U. D.
2011. Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, 29 June - 1 July 2011, 3006–3011, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/acc.2011.5991332
2010
Support-Vector Conditional Density Estimation for Nonlinear Filtering.
Krauthausen, P.; Huber, M. F.; Hanebeck, U. D.
2010. Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, 26-29 July 2010, 8 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/icif.2010.5712088
Optimal Stochastic Linearization for Range-based Localization.
Beutler, F.; Huber, M. F.; Hanebeck, U. D.
2010. Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October, 2010, 5731–5736, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2010.5649076
Semi-Analytic Stochastic Linearization for Range-Based Pose Tracking.
Beutler, F.; Huber, M. F.; Hanebeck, U. D.
2010. Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, Sept. 5-7, 2010, 44–49, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MFI.2010.5604468
Modellbasierte Quellenverfolgung in räumlich ausgedehnten Phänomenen mittels Sensoreinsatzplanung.
Kuwertz, A.; Huber, M. F.; Sawo, F.; Hanebeck, U. D.
2010. tm - Technisches Messen, 77 (10), 551–557. doi:10.1524/teme.2010.0088
Sensoreinsatzplanung zur Verfolgung von Quellen räumlich ausgedehnter Phänomene.
Kuwertz, A.; Hanebeck, U. D.; Huber, M. F.; Sawo, F.
2010. Verteilte Messsysteme. Hrsg.: F. Puente León, 179–192, KIT Scientific Publishing
2009
Instantaneous Pose Estimation using Rotation Vectors.
Beutler, F.; Huber, M. F.; Hanebeck, U. D.
2009. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009) in Taipei, Taiwan, 3413–3416, IEEE Computer Society. doi:10.1109/ICASSP.2009.4960358
Analytic Moment-based Gaussian Process Filtering.
Deisenroth, M. P.; Huber, M. F.; Hanebeck, U. D.
2009. ICML ’09. Proceedings of the 26th Annual International Conference on Machine Learning, 225–232, Association for Computing Machinery (ACM)
Distributed Greedy Sensor Scheduling for Model-based Reconstruction of Space-Time Continuous Physical Phenomena.
Huber, M. F.; Kuwertz, A.; Sawo, F.; Hanebeck, U. D.
2009. Proceedings of the 12th International Conference on Information Fusion (Fusion 2009). Seattle, Washington, USA, 06.- 09.07.2009, 102–109
Gaussian Filtering using State Decomposition Methods.
Beutler, F.; Huber, M. F.; Hanebeck, U. D.
2009. Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, 6-9 July 2009, 579–586, Institute of Electrical and Electronics Engineers (IEEE)
Dirac Mixture Approximation of Multivariate Gaussian Densities.
Hanebeck, U. D.; Huber, M. F.; Klumpp, V.
2009. Proceedings of the 48th IEEE Conference on Decision and Control (CDC). Shanghai, China, 15 - 18 December 2009. T. 7, 3851–3858, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CDC.2009.5400649
Probabilistic Instantaneous Model-Based Signal Processing applied to Localization and Tracking.
Beutler, F.; Huber, M. F.; Hanebeck, U. D.
2009. Robotics and Autonomous Systems, 57 (3), 249–258. doi:10.1016/j.robot.2008.10.013
Stochastic Nonlinear Model Predictive Control based on Gaussian Mixture Approximations.
Weissel, F.; Huber, M. F.; Hanebeck, U. D.
2009. Informatics in Control, Automation and Robotics, 239–252, Springer Verlag. doi:10.1007/978-3-540-85640-5-18
Probabilistic Framework for Sensor Management. Dissertation.
Huber, M.
2009. Universitätsverlag Karlsruhe. doi:10.5445/KSP/1000012224
2008
Priority List Sensor Scheduling using Optimal Pruning.
Huber, M. F.; Hanebeck, U. D.
2008. Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), Cologne, Germany, June 30 2008-July 3 2008, 1–8, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICIF.2008.4632231
Progressive Gaussian Mixture Reduction.
Huber, M. F.; Hanebeck, U. D.
2008. Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), Cologne, Germany, June 30 2008-July 3 2008, 1–8, Institute of Electrical and Electronics Engineers (IEEE)
Stochastic Optimal Control based on Value-Function Approximation using Sinc Interpolation.
Weissel, F.; Huber, M. F.; Brunn, D.; Hanebeck, U. D.
2008. Proceedings of the 17th IFAC World Congress (IFAC 2008), 17, Seoul, Republic of Korea, July, 2008, 8009–8014, IFAC. doi:10.3182/20080706-5-KR-1001.01352
Gaussian Filter based on Deterministic Sampling for High Quality Nonlinear Estimation.
Huber, M. F.; Hanebeck, U. D.
2008. Proceedings of the 17th World Congress The International Federation of Automatic Control Seoul, Korea, July 6-11, 2008, 13527–13532, Institute of Electrical and Electronics Engineers (IEEE). doi:10.3182/20080706-5-KR-1001.1135
Stochastic Model Predictive Control of Time-Variant Nonlinear Systems with Imperfect State Information.
Weissel, F.; Schreiter, T.; Huber, M. F.; Hanebeck, U. D.
2008. Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), Seoul, Republic of Korea, August, 2008, 40–46, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MFI.2008.4648105
On Entropy Approximation for Gaussian Mixture Random Vectors.
Huber, M. F.; Bailey, T.; Durrant-Whyte, H.; Hanebeck, U. D.
2008. Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), Seoul, Republic of Korea, August, 2008, 181–188, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MFI.2008.4648062
2007
The Hybrid Density Filter for Nonlinear Estimation based on Hybrid Conditional Density Approximation.
Huber, M. F.; Hanebeck, U. D.
2007. Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), 9-12 July 2007, Quebec, Que., Canada, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICIF.2007.4408130
Hybrid Transition Density Approximation for Efficient Recursive Prediction of Nonlinear Dynamic Systems.
Huber, M. F.; Hanebeck, U. D.
2007. International Conference on Information Processing in Sensor Networks (IPSN 2007), Cambridge, Massachusetts, USA, April 25 - 27, 2007, 283–292, Cambridge. doi:10.1145/1236360.1236398
A Closed-Form Model Predictive Control Framework for Nonlinear Noise-Corrupted Systems.
Weissel, F.; Huber, M. F.; Hanebeck, U. D.
2007. Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007), Angers, France, May, 2007. Vol. SPSMC. Ed.: J. Zaytoon, 62–69, INSTICC Press
Parameter Identification and Reconstruction for Distributed Phenomena Based on Hybrid Density Filter.
Sawo, F.; Huber, M. F.; Hanebeck, U. D.
2007. Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July, 2007, 8 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICIF.2007.4408119
The Hybrid Density Filter for Nonlinear Estimation based on Hybrid Conditional Density.
Huber, M. F.; Hanebeck, U. D.
2007. Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July, 2007, 8 S., Institute of Electrical and Electronics Engineers (IEEE)
Efficient Control of Nonlinear Noise-Corrupted Systems Using a Novel Model Predictive Control Framework.
Weissel, F.; Huber, M. F.; Hanebeck, U. D.
2007. Proceedings of the 2007 American Control Conference Marriott Marquis Hotel at Times Square, New York City, USA, July 11-13, 2007, 3751–3756, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ACC.2007.4282664
Efficient Nonlinear Measurement Updating based on Gaussian Mixture Approximation of Conditional Densities.
Huber, M. F.; Brunn, D.; Hanebeck, U. D.
2007. Proceedings of the 2007 American Control Conference (ACC 2007), New York, New York, USA, July, 2007, 4425–4430, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ACC.2007.4282269
On Sensor Scheduling in Case of Unreliable Communication.
Huber, M. F.; Stiegeler, E.; Hanebeck, U. D.
2007. INFORMATIK 2007 - the 37th Annual Conference of the Informatik 2007 : Informatik trifft Logistik; Beiträge der 37. Jahrestagung der Gesellschaft für Informatik e.V. (GI); 24. bis 27. September 2007 in Bremen. Bd. 2. Hrsg.: R. Koschke, 90–94, Gesellschaft für Informatik e.V.  (GI)
Test-Environment based on a Team of Miniature Walking Robots for Evaluation of Collaborative Control Methods.
Weissel, F.; Huber, M. F.; Hanebeck, U. D.
2007. Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007),San Diego, California, USA, November, 2007, 2474–2479, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2007.4399193
A Nonlinear Model Predictive Control Framework Approximating Noise Corrupted Systems with Hybrid Transition Densities.
Weissel, F.; Huber, M. F.; Hanebeck, U. D.
2007. Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007), New Orleans, Louisiana, USA, December, 2007, 3661–3666, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CDC.2007.4434444
2006
Closed-Form Prediction of Nonlinear Dynamic Systems by Means of Gaussian Mixture Approximation of the Transition Density.
Huber, M.; Brunn, D.; Hanebeck, U. D.
2006. Proceedings / 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 3 - 6 September 2006, Heidelberg, Germany, 98–103, IEEE Service Center. doi:10.1109/MFI.2006.265622
2005
Navigation of Walking Robots: Path Planning.
Gaßmann, B.; Huber, M.; Zöllner, J. M.; Dillmann, R.
2005. Climbing and Walking Robots: Proceedings of the 8th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR 2005). Ed.: M. O. Tokhi, 115–122, Springer Verlag. doi:10.1007/3-540-26415-9_13