Baum, Marcus

[["dc.bibliographiccitation.firstpage","4164"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","IEEE Transactions on Industrial Informatics"],["dc.bibliographiccitation.lastpage","4171"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Yang, Shishan"],["dc.contributor.author","Teich, Florian"],["dc.contributor.author","Baum, Marcus"],["dc.date.accessioned","2020-12-10T18:26:17Z"],["dc.date.available","2020-12-10T18:26:17Z"],["dc.date.issued","2019"],["dc.description.abstract","The Probability Hypotheses Density (PHD) filter is a method for tracking multiple target objects based on unlabeled detections. However, as the PHD filter employs a first-order approximation of random finite sets, it does not provide track labels, i.e., targets of consecutive time steps are not associated with each other. In this work, an intuitive and efficient labeling strategy on top of the extended target PHD filter is proposed. The approach is based on solving a network flow problem and makes use of the Wasserstein metric to account for the spatial extent of the objects. The resulting tracker is evaluated with laser scanner data from two traffic scenarios."],["dc.identifier.doi","10.1109/TII.2019.2898992"],["dc.identifier.eissn","1941-0050"],["dc.identifier.issn","1551-3203"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76028"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.issn","1551-3203"],["dc.relation.issn","1941-0050"],["dc.title","Network Flow Labeling for Extended Target Tracking PHD Filters"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.lastpage","8"],["dc.contributor.author","Janz, Thorben"],["dc.contributor.author","Leich, Andreas"],["dc.contributor.author","Junghans, Marek"],["dc.contributor.author","Gimm, Kay"],["dc.contributor.author","Yang, Shishan"],["dc.contributor.author","Baum, Marcus"],["dc.date.accessioned","2019-07-29T12:30:55Z"],["dc.date.available","2019-07-29T12:30:55Z"],["dc.date.issued","2018"],["dc.description.abstract","This work presents a method for qualitatively improving the output of an existing video and radar-based multitarget tracking system by means of post-processing. The proposed method is a novel two-pass track stitching method, which involves a breaking phase for tracklet creation and a linking phase to create putative assignments. Minimum-cost network flow techniques are utilized to find optimal tracks. The method has been tested with data from a research intersection in Braunschweig, Germany, which is operated by the German Aerospace Center for the purpose of traffic safety analysis. Furthermore, an evaluation with the publicly available MOTChallenge benchmark is provided. Improvements achieved are: 14% to 33% less ID switches and 40% less track fragmentations. Both criteria are especially relevant for traffic safety analysis."],["dc.identifier.doi","10.23919/ICIF.2018.8455280"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62140"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.publisher","IEEE"],["dc.relation.conference","21st International Conference on Information Fusion (FUSION)"],["dc.relation.eventend","2018-07-13"],["dc.relation.eventlocation","Cambridge, UK"],["dc.relation.eventstart","2018-07-10"],["dc.relation.isbn","978-0-9964527-6-2"],["dc.relation.iserratumof","yes"],["dc.title","Post-Processing of Multi-Target Trajectories for Traffic Safety Analysis"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","4720"],["dc.bibliographiccitation.issue","18"],["dc.bibliographiccitation.journal","IEEE Transactions on Signal Processing"],["dc.bibliographiccitation.lastpage","4729"],["dc.bibliographiccitation.volume","67"],["dc.contributor.author","Yang, Shishan"],["dc.contributor.author","Baum, Marcus"],["dc.date.accessioned","2020-12-10T18:26:23Z"],["dc.date.available","2020-12-10T18:26:23Z"],["dc.date.issued","2019"],["dc.description.abstract","Extended object tracking considers the simultaneous estimation of the kinematic state and the shape parameters of a moving object based on a varying number of noisy detections. A main challenge in extended object tracking is the nonlinearity and high-dimensionality of the estimation problem. This work presents compact closed-form expressions for a recursive Kalman filter that explicitly estimates the orientation and axes lengths of an extended object based on detections that are scattered over the object surface. Existing approaches are either based on Monte Carlo approximations or do not allow for explicitly maintaining all ellipse parameters. The performance of the novel approach is demonstrated with respect to the state-of-the-art by means of simulations."],["dc.identifier.doi","10.1109/TSP.78"],["dc.identifier.eissn","1941-0476"],["dc.identifier.issn","1053-587X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76066"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.issn","1053-587X"],["dc.relation.issn","1941-0476"],["dc.title","Tracking the Orientation and Axes Lengths of an Elliptical Extended Object"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]