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Real-time Computer Vision Group

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Profile

Dealing with the topic of efficient computer vision, our group has a long and successful history at the chair. Our current focus lies on the technologies for driver assistance systems, which represent an important and challenging field of application. These intelligent systems analyse the vehicle´s environment via different types of sensors, for instance video and radar, thus, increasing safety and comfort for the driver.

Driver assistance systems have to provide reliable results within natural, and therefore, complex and highly dynamic environments. At the same time mobile computers suffer from limited resources. These circumstances make the given tasks very demanding.

In the presented research areas our group is able to look back on numerous projects in cooperation with companies from the automotive industry. The group has excellent know-how in computer vision using an own development framework for scene representations for the integration of a large set of modules. Particularly, in the fields of machine learning, cognitive systems and optimization we benefit from the institute´s interdisciplinary character.

Research areas

Available resources

Job Offers

Theses

► Ongoing:

  • Steven Heitmeier. Analyse von Bewegungsmustern eines Tennisspielers
    durch videobasiertes Tracking der Fußpositionen
    . Bachelorarbeit

► Completed:



 

Publications

 

A. Ibisch, S.Houben, M. Schlipsing, R. Kesten, P. Reimche, F. Schuller and H. Altinger.Towards Highly Automated Driving in a Parking Garage: General Object Localization and Tracking Using An Environment-Embedded Camera System. In Proceedings of the IEEE Intelligent Vehicles Symposium. 2014.[PDF] [Poster]
S. Houben. Towards the Intrinsic Self-Calibration of a Vehicle-Mounted Omni-Directional Radially Symmetric Camera. In Proceedings of the IEEE Intelligent Vehicles Symposium, 2014. [Paper] [Poster]
M. Schlipsing, J. Salmen, M. Tschentscher, and C. Igel. Adaptive Pattern Recognition in Real-time Video-based Soccer Analysis. In Journal of Real-Time Image Processing, 2014. [Springer-DOI]
S. Houben, M. Komar, A. Hohm, S. Lüke, M. Neuhausen, and M. Schlipsing. On-Vehicle Video-Based Parking Lot Recognition with Fisheye Optics. In Proceedings of the International IEEE Conference on Intelligent Transport Systems, pages 7-12, 2013. [Paper]
S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel. Detection of Traffic Signs in Real-World Images: The German Traffic Sign Detection Benchmark. In Proceedings of the IEEE International Joint Conference on Neural Networks, no. 1288, 2013. [Paper]
M. Schlipsing, J. Salmen, and C. Igel. Echtzeit-Videoanalyse im Fußball - Ein Live-System zum Spieler-Tracking. Künstliche Intelligenz, 27(3), pages 235–240. 2013. [Springer-DOI]
L. Caup, J. Salmen, I. Muharemovic, and S. Houben. Video-based Trailer Detection and Articulation Estimation. In Proceedings of the IEEE Intelligent Vehicles Symposium, pages 1179–1184. 2013. [Paper] [Poster]
M. Michael, J. Salmen, J. Stallkamp, and M. Schlipsing. Real-time Stereo Vision: Optimizing Semi-Global Matching. In Proceedings of the IEEE Intelligent Vehicles Symposium, pages 1197–1202. 2013. [Paper]
A. Ibisch, S. Stümper, H. Altinger, M. Neuhausen, M. Tschentscher, M. Schlipsing, J. Salmen, and A. Knoll. Autonomous Driving in a Parking Garage: Vehicle Localization and Tracking Using Environment-embedded LIDAR Sensors. In Proceedings of the IEEE Intelligent Vehicles Symposium, pages 829–834. 2013. [Paper] [Poster]
M. Tschentscher, M. Neuhausen, C. Koch, M. König, J. Salmen, and M. Schlipsing. Comparing Image Features and Machine Learning Algorithms for Real-time Parking-space Classifiaction. In Proceedings of the ASCE International Workshop on Computing in Civil Engineering, pages 363–370. 2013. [Paper]
M. Tschentscher and M. Neuhausen. Video-based Parking-space Detection. In Proceedings of the Forum Bauinformatik, pages 159–166. 2012. [Paper]
M. Schlipsing, J. Salmen, B. Lattke, K. Schröter, and H. Winner. Roll Angle Estimation for Motorcycles: Comparing Video and Inertial Sensor Approaches. In Proceedings of the IEEE Intelligent Vehicles Symposium, pages 500–505. 2012. [Paper] [Poster]
J. Salmen, S. Houben, and M. Schlipsing. Google Street View Images Support the Development of Vision-Based Driver Assistance Systems. In Proceedings of the IEEE Intelligent Vehicles Symposium, pages 891–895. 2012. [Paper] [Poster]
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. Man vs. Computer: Benchmarking Machine Learning Algorithms for Traffic Sign Recognition. Neural Networks, volume 32, pages 323–332. 2012. [PDF]
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition. In Proceedings of the IEEE International Joint Conference on Neural Networks, pages 1453–1460, 2011. [PDF]
S. Houben. A single target voting scheme for traffic sign detection. In Proceedings of the IEEE Intelligent Vehicles Symposium, pages 124–129, 2011. [PDF]
M. Schlipsing, J. Schepanek, and J. Salmen. Video-Based Roll Angle Estimation for Two-Wheeled Vehicles. In Proceedings of the IEEE Intelligent Vehicles Symposium, pages 876–881, 2011. [Paper] [Poster]
J. Salmen, L. Caup, and C. Igel. Real-time estimation of optical flow based on optimized Haar wavelet features. In Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization, pages 448–461, 2011. [PDF]
J. Salmen, M. Schlipsing, and C. Igel. Efficient Update of the Covariance Matrix Inverse in Iterated Linear Discriminant Analysis. Pattern Recognition Letters, 31:1903–1907, 2010. [PDF]
J. Salmen, M. Schlipsing, J. Edelbrunner, S. Hegemann, and S. Lueke. Real-Time Stereo Vision: Making more out of Dynamic Programming. In Proceedings of the International Conference on Computer Analysis of Images and Patterns, pages 1096–1103, 2009. [PDF]
J. Salmen, T. Suttorp, J. Edelbrunner, and C. Igel. Evolutionary optimization of wavelet feature sets for real-time pedestrian classification. In Proceedings of the IEEE Conference on Hybrid Intelligent Systems, pages 222–227, 2007. [PDF]
T. Suttorp and T. Bücher. Learning of Kalman filter parameters for lane detection. In Proceedings of the IEEE Intelligent Vehicles Symposium, pages 552–557, 2006. [PDF]
T. Suttorp and T. Bücher. Robust vanishing point estimation for driver assistance. In Proceedings of the IEEE Conference on Intelligent Transportation Systems, pages 1550–1555, 2006. [PDF]
A. Gepperth and S. Roth. Applications of multi-objective structure optimization. Neurocomputing, 69:701–713, 2006.
A. Gepperth. Visual object classification by sparse convolutional neural networks. In Proceedings of the European Symposium on Artificial Neural Networks, pages 179–185, 2006. [PDF]
A. Gepperth. Object detection and feature base learning by sparse convolutional neural networks. In Proceedings of the 2nd IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, Lecture notes in artificial intelligence 4807, pages 221–231, 2006.
A. Gepperth, J. Edelbrunner, and T. Bücher. Real-time detection of cars in video sequences. In Proceedings of the IEEE Intelligent Vehicles Symposium, pages 625–631, 2005. [PDF]
A. Gepperth, J. Edelbrunner, and T. Bücher. Videobasierte Klassifikation von Fahrzeugen in Echtzeit. In Tagungsband des 3. Workshops Fahrerassistenzsysteme, pages 121–128, 2005.
A. Gepperth and S. Roth. Applications of multi-objective structure optimization. In Proceedings of the European symposium on artificial neural networks, pages 279–284, 2005.
S. Roth, A. Gepperth, and C. Igel. Multi-objective structure optimization for visual object detection. In Multi-objective Machine Learning, volume 16 of Studies in Computational Intelligence, pages 629–655, 2005. [PDF]
A. Pellecchia, C. Igel, J. Edelbrunner, and G. Schöner. Making Driver Modeling Attractive. IEEE Intelligent Systems, 20(2):8–12, 2005.
T. Bücher, C. Curio, J. Edelbrunner, C. Igel, D. Kastrup, I. Leefken, G. Lorenz, A. Steinhage, and W. von Seelen. Image processing and behaviour planning for intelligent vehicles. IEEE Transactions on Industrial electronics, 90(1):62–75, 2003.

 

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