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Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
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Barrois, B., Hristova, S., Wöhler, C., Kummert, F. & Hermes, C. | 3D Pose Estimation of Vehicles Using a Stereo Camera | 2009 | Intelligent Vehicles Symposium (IV'09) | inproceedings | URL |
Abstract: This study introduces an approach to three- dimensional vehicle pose estimation using a stereo camera system. After computation of stereo and optical flow on the investigated scene, a four-dimensional clustering approach separates the static from the moving objects in the scene. The iterative closest point algorithm (ICP) estimates the vehicle pose using a cuboid as a weak vehicle model. In contrast to classical ICP optimisation a polar distance metric is used which especially takes into account the error distribution of the stereo measurement process. The tracking approach is based on tracking-by-detection such that no temporal filtering is used. The method is evaluated on seven different real-world sequences, where different stereo algorithms, baseline distances, distance metrics, and optimisation algorithms are examined. The results show that the proposed polar distance metric yields a higher accuracy for yaw angle estimation of vehicles than the common Euclidean distance metric, especially when using pixel-accurate stereo points. |
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BibTeX:
@inproceedings{Barrois09, author = {Barrois, Björn and Hristova, Stela and Wöhler, Christian and Kummert, Franz and Hermes, Christoph}, title = {3D Pose Estimation of Vehicles Using a Stereo Camera}, booktitle = {Intelligent Vehicles Symposium (IV'09)}, year = {2009}, url = {files/Barrois09.pdf} } |
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Hermes, C. | Aktionserkennung und -prädiktion mittels Trajektorienklassifikation [BibTeX] |
2012 | School: Bielefeld University, Applied Informatics | phdthesis | URL |
BibTeX:
@phdthesis{Hermes12, author = {Hermes, Christoph}, title = {Aktionserkennung und -prädiktion mittels Trajektorienklassifikation}, school = {Bielefeld University, Applied Informatics}, year = {2012}, url = {files/Hermes12.pdf} } |
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Hermes, C., Barth, A., Wöhler, C. & Kummert, F. | Object Motion Analysis and Prediction in Stereo Image Sequences | 2009 | Proc. Oldenburger 3D-Tage | inproceedings | URL |
Abstract: Future driver assistance systems will have to cope with complex traffic situations, espe- cially at intersections. To detect potentially hazardous situations as early as possible, it is therefore desirable to know the position and motion of oncoming vehicles for several sec- onds in advance. For this purpose, we propose a combined approach that tracks the vehicle position and orientation over time based on a box model, where the vehicle motion state is predicted several seconds ahead based on simultaneous tracking of multiple hypotheses with a particle filter framework. The scene is observed by a stereo camera mounted on the ego-vehicle. Compared to a traditional constant acceleration and curve radius prediction model, we show that the accuracy of the proposed particle filter approach is superior during turning manoeuvres displaying complex motion patterns. |
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BibTeX:
@inproceedings{Hermes09a, author = {Hermes, Christoph and Barth, Alexander and Wöhler, Christian and Kummert, Franz}, title = {Object Motion Analysis and Prediction in Stereo Image Sequences}, booktitle = {Proc. Oldenburger 3D-Tage}, year = {2009}, url = {files/Hermes09a.pdf} } |
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Hermes, C., Einhaus, J., Hahn, M., Wöhler, C. & Kummert, F. | Vehicle tracking and motion prediction in complex urban scenarios | 2010 | IEEE Intelligent Vehicles Symposium (IV) , pp. 26-33 | inproceedings | URL |
Abstract: The recognition of potentially hazardous situations on road intersections is an indispensable skill of future driver assistance systems. In this context, this study focuses on the task of vehicle tracking in combination with a long-term motion prediction (1–2 s into the future) in a dynamic scenario. A motion-attributed stereo point cloud obtained using computationally efficient feature-based methods represents the scene, relying on images of a stereo camera system mounted on a vehicle. A two-stage mean-shift algorithm is used for detection and tracking of the traffic participants. A hierarchical setup depending on the history of the tracked object is applied for prediction. This includes prediction by optical flow, a standard kinematic prediction, and a particle filter based motion pattern method relying on learned object trajectories. The evaluation shows that the proposed system is able to track the road users in a stable manner and predict their positions at least one order of magnitude more accurately than a standard kinematic prediction method. | |||||
BibTeX:
@inproceedings{Hermes10, author = {Hermes, Christoph and Einhaus, Julian and Hahn, Markus and Wöhler, Christian and Kummert, Franz}, title = {Vehicle tracking and motion prediction in complex urban scenarios}, booktitle = {IEEE Intelligent Vehicles Symposium (IV) }, year = {2010}, pages = {26--33}, url = {files/Hermes10.pdf} } |
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Hermes, C., Hahn, M., Einhaus, J., Wöhler, C. & Kummert, F. | Tracking and Motion Prediction of Vehicles in Complex Urban Traffic Scenes [BibTeX] |
2010 | Proc. 4. Tagung Sicherheit durch Fahrerassistenz | inproceedings | URL |
BibTeX:
@inproceedings{Hermes10a, author = {Hermes, Christoph and Hahn, Markus and Einhaus, Julian and Wöhler, Christian and Kummert, Franz}, title = {Tracking and Motion Prediction of Vehicles in Complex Urban Traffic Scenes}, booktitle = {Proc. 4. Tagung Sicherheit durch Fahrerassistenz}, year = {2010}, url = {files/Hermes10a.pdf} } |
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Hermes, C., Wöhler, C., Schenk, K. & Kummert, F. | Long-term Vehicle Motion Prediction | 2009 | IEEE Intelligent Vehicles Symposium , pp. 652-657 | inproceedings | URL |
Abstract: Future driver assistance systems will have to cope with complex traffic situations, especially in the road crossing scenario. To detect potentially hazardous situations as early as possible, it is therefore desirable to know the position and motion of the ego-vehicle and vehicles around it for several seconds in advance. For this purpose, we propose in this study a long-term prediction approach based on a combined trajectory classification and particle filter framework. As a measure for the similarity between trajectories, we introduce the quaternion-based rotationally invariant longest common subsequence (QRLCS) metric. The trajectories are classified by a radial basis function (RBF) classifier with an architecture that is able to process trajectories of arbitrary non-uniform length. The particle filter framework simultaneously tracks and assesses a large number of motion hypotheses (∼102 ), where the class-specific probabilities estimated by the RBF classifier are used as a-priori probabilities for the hypotheses of the particle filter. The hypotheses are clustered with a mean-shift technique and are assigned a likelihood value. Motion prediction is performed based on the cluster centre with the highest likelihood. While traditional motion prediction based on curve radius and acceleration is inaccurate especially during turning manoeuvres, we show that our approach achieves a reasonable motion prediction even for long prediction intervals of 3 s for these complex motion patterns. | |||||
BibTeX:
@inproceedings{Hermes09, author = {Hermes, Christoph and Wöhler, Christian and Schenk, Konrad and Kummert, Franz}, title = {Long-term Vehicle Motion Prediction}, booktitle = {IEEE Intelligent Vehicles Symposium }, year = {2009}, pages = {652-657}, url = {files/Hermes09.pdf} } |
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Hermes, C., Wiest, Jü., Wöhler, C., Kreßel, U. & Kummert, F. | Manifold-based Motion Prediction | 2011 | Proc. 6. Dortmunder Auto-Tag | inproceedings | URL |
Abstract: Advanced driver assistance systems (ADAS) have to cope with complex traffic situations, especially in the road crossing scenario. To detect potentially hazardous situations as early as possible, it is therefore desirable to know the position and motion of the ego-vehicle and vehicles around it for several seconds in advance. The standard motion prediction approach is the so-called kinematic prediction, i.e. constant yaw rate and constant acceleration, but it systematically fails at road intersections. The proposed approach uses previously observed driving manoeuvres to find a low-dimensional representation of common motion patterns. A probabilistic filter with mode detection tracks the vehicle's driving path and simultaneously predicts its motion several seconds ahead. Evalu ated on a Differential GPS trajectory dataset, the proposed system shows significantly better results than the standard prediction approach for different prediction horizons. |
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BibTeX:
@inproceedings{Hermes11, author = {Hermes, Christoph and Wiest, Jürgen and Wöhler, Christian and Kreßel, Ulrich and Kummert, Franz}, title = {Manifold-based Motion Prediction}, booktitle = {Proc. 6. Dortmunder Auto-Tag}, year = {2011}, url = {files/Hermes11.pdf} } |
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Käfer, E., Hermes, C., Wöhler, C., Kummert, F. & Ritter, H. | Recognition and Prediction of Situations in Urban Traffic Scenarios | 2010 | Proc. Int. Conf. on Pattern Recognition, pp. 4234-4237 | inproceedings | URL |
Abstract: The recognition and prediction of intersection situ- ations and an accompanying threat assessment are an indispensable skill of future driver assistance systems. This study focuses on the recognition of situations in- volving two vehicles at intersections. For each vehi- cle, a set of possible future motion trajectories is esti- mated and rated based on a motion database for a time interval of 24 s ahead. Possible situations involving two vehicles are generated by a pairwise combination of these individual motion trajectories. An interaction model based on the mutual visibility of the vehicles and the assumption that a driver will attempt to avoid a collision is used to rate possible situations. The cor- respondingly favoured situations are classified with a probabilistic framework. The proposed method is eval- uated on a real-world differential GPS data set acquired during a test drive of 10 km, including three road in- tersections. Our method is typically able to recognise the situation correctly about 1.53 s before the distance of the vehicles to the intersection centre becomes mini- mal. |
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BibTeX:
@inproceedings{Kaefer10a, author = {Käfer, Eugen and Hermes, Christoph and Wöhler, Christian and Kummert, Franz and Ritter, Helge}, title = {Recognition and Prediction of Situations in Urban Traffic Scenarios}, booktitle = {Proc. Int. Conf. on Pattern Recognition}, year = {2010}, pages = {4234--4237}, url = {files/Kaefer10a.pdf} } |
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Käfer, E., Hermes, C., Wöhler, C., Ritter, H. & Kummert, F. | Recognition of situation classes at road intersections | 2010 | IEEE International Conference on Robotics and Automation (ICRA), pp. 3960-3965 | inproceedings | URL |
Abstract: The recognition and prediction of situations is an indispensable skill of future driver assistance systems. This study focuses on the recognition of situations involving two vehicles at intersections. For each vehicle, a set of possible future motion trajectories is estimated and rated based on a motion database for a time interval of 2-4 seconds ahead. Realistic situations are generated by a pairwise combination of these individual motion trajectories and classified according to nine categories with a polynomial classifier. In the proposed framework, situations are penalised for which the time to collision significantly exceeds the typical human reaction time. The correspondingly favoured situations are combined by a probabilistic framework, resulting in a more reliable situation recognition and collision detection than obtained based on independent motion hypotheses. The proposed method is evaluated on a real-world differential GPS data set acquired during a test drive of 10 km, including three road intersections. Our method is typically able to recognise the situation correctly about 1-2 seconds before the distance to the intersection centre becomes minimal. | |||||
BibTeX:
@inproceedings{Kaefer10, author = {Käfer, E. and Hermes, C. and Wöhler, C. and Ritter, H. and Kummert, F.}, title = {Recognition of situation classes at road intersections}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2010}, pages = {3960--3965}, url = {files/Kaefer10.pdf} } |
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Keller, C., Hermes, C. & Gavrila, D. | Will the Pedestrian Cross? Probabilistic Path Prediction Based on Learned Motion Features | 2011 | Vol. 6835Pattern Recognition, pp. 386-395 |
incollection | URL |
Abstract: Future vehicle systems for active pedestrian safety will not only require a high recognition performance, but also an accurate analysis of the developing traffic situation. In this paper, we present a system for pedestrian action classification (walking vs. stopping) and path prediction at short, sub-second time intervals. Apart from the use of positional cues, obtained by a pedestrian detector, we extract motion features from dense optical flow. These augmented features are used in a probabilistic trajectory matching and filtering framework. The vehicle-based system was tested in various traffic scenes. We compare its performance to that of a state-of-the-art IMM Kalman filter (IMM-KF), and for the action classification task, to that of human observers, as well. Results show that human performance is best, followed by that of the proposed system, which outperforms the IMM-KF and the simpler system variants. |
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BibTeX:
@incollection{Keller11, author = {Keller, Christoph and Hermes, Christoph and Gavrila, Dariu}, title = {Will the Pedestrian Cross? Probabilistic Path Prediction Based on Learned Motion Features}, booktitle = {Pattern Recognition}, publisher = {Springer Berlin / Heidelberg}, year = {2011}, volume = {6835}, pages = {386--395}, url = {files/Keller11.pdf} } |
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Käfer, E., Hermes, C., Wöhler, C., Ritter, H. & Kummert, F. | Situation analysis at road intersections [BibTeX] |
2010 | Proc. 5. Dortmunder Auto-Tag | inproceedings | |
BibTeX:
@inproceedings{Kaefer10b, author = {Käfer, Eugen and Hermes, Christoph and Wöhler, Christian and Ritter, Helge and Kummert, Franz}, title = {Situation analysis at road intersections}, booktitle = {Proc. 5. Dortmunder Auto-Tag}, year = {2010} } |
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Schmidt, J., Wöhler, C., Krüger, L., Gövert, T. & Hermes, C. | 3D Scene Segmentation and Object Tracking in Multiocular Image Sequences | 2007 | The 5th Int. Conf. on Computer Vision Systems Conf. Paper | inproceedings | URL |
Abstract: In this contribution we describe a vision-based system for the 3D detection and tracking of moving persons and objects in complex scenes. A 3D point cloud of the scene is extracted by a combined stereo technique consisting of a correlation-based block-matching approach and a spacetime stereo approach based on spatio-temporally local intensity modelling. Hence, the result of stereo analysis is a 3D point cloud attributed with motion information. For localising persons and objects in the scene the point cloud is segmented into meaningful clusters by applying a hierarchical clustering algorithm, using velocity information as an additional discrimination criterion. Initial object hypotheses are obtained by partitioning the observed scene with cylinders, including the tracking results of the previous frame. Multidimensional unconstrained nonlinear minimisation is then applied to refine the position, velocity and size of the initial cylinder in the scene, such that neighbouring clusters with similar velocity vectors are grouped to form a compact object. A particle filter is applied to select hypotheses which generate consistent trajectories. The described system is evaluated based on a tabletop sequence and several real-world sequences acquired in an industrial production environment, based on manually obtained ground truth data. We find that even in the presence of moving objects closely neighbouring the person, all objects are detected and tracked in a robust and stable manner. The average tracking accuracy is of the order of several percent of the distance to the scene. | |||||
Review: - combination of Spacetime-Stereo and Correlation-based stero - clustering in 4 dimensions (x,y,z,v) - partcle filtering for tracking | |||||
BibTeX:
@inproceedings{Schmidt07, author = {Joachim Schmidt and Christian Wöhler and Lars Krüger and Tobias Gövert and Christoph Hermes}, title = {3D Scene Segmentation and Object Tracking in Multiocular Image Sequences}, booktitle = {The 5th Int. Conf. on Computer Vision Systems Conf. Paper}, year = {2007}, url = {files/Schmidt07.pdf} } |
Created by JabRef on 27/08/2013.
file | file size | comments |
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Barrois09.pdf | 2.51 MB | none |
Hermes09.pdf | 387.31 KB | none |
Hermes09a.pdf | 412.48 KB | none |
Hermes10.pdf | 1.78 MB | none |
Hermes11.pdf | 162.25 KB | none |
Hermes12.pdf | 5.57 MB | none |
Kaefer10.pdf | 324.06 KB | none |
Kaefer10a.pdf | 453.98 KB | none |
Keller11.pdf | 1.13 MB | none |
Schmidt07.pdf | 1.03 MB | none |