Stefan Gehrig and Alexander Barth and Nicolai Schneider and Jan Siegemund, "A Multi-Cue Approach for Stereo-Based Object Confidence Estimation", In Intelligent Robots and Systems (IROS). Vilamoura, Portugal, pp. 3055 - 3060. 2012.

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In this contribution we present an approach to compute object confidences for stereo-vision-based object tracking schemes. Meaningful object confidences help to reduce false alarm rates of safety systems and improve the downstream system performance for modules such as sensor fusion and situation analysis. Several cues from stereo vision and from the tracking process are fused in a Bayesian manner. An evaluation on a 38,000 frames urban drive shows the effectiveness of the approach compared to the same object tracking scheme with simple heuristics for the object confidence. Within the evaluation, also the relevance of occurring phantoms is considered by computing the collision risk. The proposed confidence measures reduce the number of predicted imminent collisions from 86 to 0 maintaining almost the same system availability.

@inproceedings{Gehrig2012Multi,
  author = {Gehrig, Stefan and Barth, Alexander and Schneider, Nicolai and Siegemund, Jan},
  title = {A Multi-Cue Approach for Stereo-Based Object Confidence Estimation},
  booktitle = {Intelligent Robots and Systems (IROS)},
  year = {2012},
  pages = {3055 -- 3060},
  doi = {10.1109/IROS.2012.6385455}
}

Ribana Roscher and Jan Siegemund and Falko Schindler and Wolfgang Förstner, "Object Tracking by Segmentation Using Incremental Import Vector Machines" 2012.

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We propose a framework for object tracking in image sequences, following the concept of tracking-by-segmentation. The separation of object and background is achieved by a consecutive semantic superpixel segmentation of the images, yielding tight object boundaries. I.e., in the first image a model of the object's characteristics is learned from an initial, incomplete annotation. This model is used to classify the superpixels of subsequent images to object and background employing graph-cut. We assume the object boundaries to be tight-fitting and the object motion within the image to be affine. To adapt the model to radiometric and geometric changes we utilize an incremental learner in a co-training scheme. We evaluate our tracking framework qualitatively and quantitatively on several image sequences.

@techreport{Roscher2012Object,
  author = {Roscher, Ribana and Siegemund, Jan and Schindler, Falko and F\"orstner, Wolfgang},
  title = {Object Tracking by Segmentation Using Incremental Import Vector Machines},
  year = {2012}
}

Jan Siegemund and Uwe Franke and Wolfgang Förstner, "A Temporal Filter Approach for Detection and Reconstruction of Curbs and Road Surfaces based on Conditional Random Fields", In IEEE Intelligent Vehicles Symposium (IV)., June, 2011., pp. 637-642. IEEE Computer Society. 2011.

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A temporal filter approach for real-time detection and reconstruction of curbs and road surfaces from 3D point clouds is presented. Instead of local thresholding, as used in many other approaches, a 3D curb model is extracted from the point cloud. The 3D points are classified to different parts of the model (i.e. road and sidewalk) using a temporally integrated Conditional Random Field (CRF). The parameters of curb and road surface are then estimated from the respectively assigned points, providing a temporal connection via a Kalman filter. In this contribution, we employ dense stereo vision for data acquisition. Other sensors capturing point cloud data, e.g. lidar, would also be suitable. The system was tested on real-world scenarios, showing the advantages over a temporally unfiltered version, due to robustness, accuracy and computation time. Further, the lateral accuracy of the system is evaluated. The experiments show the system to yield highly accurate results, for curved and straight-line curbs, up to distances of 20 meters from the camera.

@inproceedings{Siegemund2011Temporal,
  author = {Siegemund, Jan and Franke, Uwe and F\"orstner, Wolfgang},
  title = {A Temporal Filter Approach for Detection and Reconstruction of Curbs and Road Surfaces based on Conditional Random Fields},
  booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
  publisher = {IEEE Computer Society},
  year = {2011},
  pages = {637-642},
  doi = {10.1109/IVS.2011.5940447}
}

Alexander Barth and Jan Siegemund and Annemarie Meißner and Uwe Franke and Wolfgang Förstner, "Probabilistic Multi-Class Scene Flow Segmentation for Traffic Scenes", In Pattern Recognition (Symposium of DAGM). Goesele, M. and Roth, S. and Kuijper, A. and Schiele, B. and Schindler, K. (Eds.), pp. 503-512. Springer. 2010.

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A multi-class traffic scene segmentation approach based on scene flow data is presented. Opposed to many other approaches using color or texture features, our approach is purely based on dense depth and 3D motion information. Using prior knowledge on tracked objects in the scene and the pixel-wise uncertainties of the scene flow data, each pixel is assigned to either a particular moving object class (tracked/unknown object), the ground surface, or static background. The global topological order of classes, such as objects are above ground, is locally integrated into a conditional random field by an ordering constraint. The proposed method yields very accurate segmentation results on challenging real world scenes, which we made publicly available for comparison.

@inproceedings{Barth2010Probabilistic,
  author = {Barth, Alexander and Siegemund, Jan and Mei{\ss}ner, Annemarie and Franke, Uwe and F\"orstner, Wolfgang},
  editor = {Goesele, M. and Roth, S. and Kuijper, A. and Schiele, B. and Schindler, K.},
  title = {Probabilistic Multi-Class Scene Flow Segmentation for Traffic Scenes},
  booktitle = {Pattern Recognition (Symposium of DAGM)},
  publisher = {Springer},
  year = {2010},
  pages = {503--512},
  note = {Darmstadt},
  doi = {10.1007/978-3-642-15986-2_51}
}

Maximilian Muffert and Jan Siegemund and Wolfgang Förstner, "The estimation of spatial positions by using an omnidirectional camera system", In 2nd International Conference on Machine Control & Guidance., March, 2010., pp. 95-104. 2010.

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With an omnidirectional camera system, it is possible to take 360-degree views of the surrounding area at each camera position. These systems are used particularly in robotic applications, in autonomous navigation and supervision technology for ego-motion estimation. In addition to the visual capture of the environment itself, we can compute the parameters of orientation and position from image sequences, i.e. we get three parameters of position and three of orientation (yaw rate, pitch and roll angle) at each time of acquisition. The aim of the presented project is to investigate the quality of the spatial trajectory of a mobile survey vehicle from the recorded image sequences. In this paper, we explain the required photogrammetric background and show the advantages of omnidirectional camera systems for this task. We present the first results on our test set and discuss alternative applications for omnidirectional cameras.

@inproceedings{Muffert2010estimation,
  author = {Muffert, Maximilian and Siegemund, Jan and F\"orstner, Wolfgang},
  title = {The estimation of spatial positions by using an omnidirectional camera system},
  booktitle = {2nd International Conference on Machine Control \& Guidance},
  year = {2010},
  pages = {95--104}
}

Jan Siegemund and David Pfeiffer and Uwe Franke and Wolfgang Förstner, "Curb Reconstruction using Conditional Random Fields", In IEEE Intelligent Vehicles Symposium (IV)., June, 2010., pp. 203-210. IEEE Computer Society. 2010.

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This paper presents a generic framework for curb detection and reconstruction in the context of driver assistance systems. Based on a 3D point cloud, we estimate the parameters of a 3D curb model, incorporating also the curb adjacent surfaces, e.g. street and sidewalk. We apply an iterative two step approach. First, the measured 3D points, e.g., obtained from dense stereo vision, are assigned to the curb adjacent surfaces using loopy belief propagation on a Conditional Random Field. Based on this result, we reconstruct the surfaces and in particular the curb. Our system is not limited to straight-line curbs, i.e. it is able to deal with curbs of different curvature and varying height. The proposed algorithm runs in real-time on our demon- strator vehicle and is evaluated in urban real-world scenarios. It yields highly accurate results even for low curbs up to 20 m distance.

@inproceedings{Siegemund2010Curb,
  author = {Siegemund, Jan and Pfeiffer, David and Franke, Uwe and F\"orstner, Wolfgang},
  title = {Curb Reconstruction using Conditional Random Fields},
  booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
  publisher = {IEEE Computer Society},
  year = {2010},
  pages = {203--210},
  doi = {10.1109/IVS.2010.5548096}
}

Alexander Barth and Jan Siegemund and Uwe Franke and Wolfgang Förstner, "Simultaneous Estimation of Pose and Motion at Highly Dynamic Turn Maneuvers", In 31th Annual Symposium of the German Association for Pattern Recognition (DAGM). Denzler, J. and Notni, G. (Eds.) Jena, Germany, pp. 262-271. Springer. 2009.

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Abstract. The (Extended) Kalman filter has been established as a stan- dard method for object tracking. While a constraining motion model stabilizes the tracking results given noisy measurements, it limits the ability to follow an object in non-modeled maneuvers. In the context of a stereo-vision based vehicle tracking approach, we propose and compare three different strategies to automatically adapt the dynamics of the fil- ter to the dynamics of the object. These strategies include an IMM-based multi-filter setup, an extension of the motion model considering higher order terms, as well as the adaptive parametrization of the filter vari- ances using an independent maximum likelihood estimator. For evalua- tion, various recorded real world trajectories and simulated maneuvers, including skidding, are used. The experimental results show significant improvements in the simultaneous estimation of pose and motion.

@inproceedings{Barth2009Simultaneous,
  author = {Barth, Alexander and Siegemund, Jan and Franke, Uwe and F\"orstner, Wolfgang},
  editor = {Denzler, J. and Notni, G.},
  title = {Simultaneous Estimation of Pose and Motion at Highly Dynamic Turn Maneuvers},
  booktitle = {31th Annual Symposium of the German Association for Pattern Recognition (DAGM)},
  publisher = {Springer},
  year = {2009},
  pages = {262--271},
  doi = {10.1007/978-3-642-03798-6_27}
}

Jan Siegemund, "Trajektorienrekonstruktion von bewegten Objekten aus Stereobildfolgen". Thesis at: University of Bonn
In Zusammenarbeit mit dem Institut für Informatik der Universität Bonn. 2008.

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Die vorliegende Arbeit beschäftigt sich mit der Rekonstruktion der räumlichen Trajektorienparameter bewegter Objekte anhand von kalibrierten Stereobildsequenzen. Zur Lösung dieses Problems wird ein Verfahren auf der Grundlage eines robusten Ausgleichungsmodells eingeführt. Als Eingabedaten dienen vorsegmentierte Bildpunkte des Objektes mit bekannter stereoskopischer und temporaler Zuordnung. Auf Basis dieser Bildinformation wird zusätzlich zu den Trajektorienparametern eine dreidimensionale Punktwolke in einem lokalen Objektsystem geschätzt, welche Hinweise auf Form und Ausmaße des beobachteten Objektes liefert. Darüber hinaus werden Techniken zur Steigerung der Effizienz und Robustheit des Verfahrens vorgestellt und es wird erläutert, wie mögliches Vorwissen in den Ausgleichungsprozess eingebracht werden kann. Der Anwendungsfokus in Beispielen und Ergebnissen liegt auf der Bestimmung der Trajektorien von Fremdfahrzeugen mittels Eigenfahrzeugsensorik zum Zwecke der Kollisionsvermeidung. Diese Informationen sind für Fahrassistenzsysteme von großer Bedeutung und für die Daimler AG als Kooperationspartner dieser Arbeit von besonderem Interesse. Das Verfahren selbst wird jedoch auf kein spezielles Anwendungsgebiet beschränkt. Anhand von Experimenten auf simulierten Szenen wird ein systematischer Fehler in den geschätzten Objektpositionen beobachtet. Das Auftreten dieses Fehlers wird motiviert und Methoden zur Behebung werden vorgestellt.Weiterhin zeigen Experimente auf realen Aufnahmen die Notwendigkeit einer zeitlichen Glättung der geschätzten Trajektorienparameter. Aus diesem Grund wird eine adaptive Glättungsmethode eingeführt, deren Strenge darüber hinaus anwendungsbezogen gesteuert werden kann. Die Ergebnisse zeigen, dass das Verfahren, trotz hoher Ausreißeranteile in den Eingabedaten, im Stande ist, die Bewegungstrajektorie eines Objektes mit hoher Genauigkeit und Robustheit zu bestimmen und gleichzeitig die dreidimensionale Form des beobachteten Objektes zu rekonstruieren.

@mastersthesis{Siegemund2008Trajektorienrekonstruktion,
  author = {Siegemund, Jan},
  title = {Trajektorienrekonstruktion von bewegten Objekten aus Stereobildfolgen},
  school = {University of Bonn
In Zusammenarbeit mit dem Institut f\"ur Informatik der Universit\"at Bonn},
  year = {2008},
  note = {Betreuung: Prof. Dr.-Ing. Wolfgang F\"orstner, Prof. Dr. Daniel Cremers}
}