Baum, Marcus

[["dc.bibliographiccitation.firstpage","98"],["dc.bibliographiccitation.lastpage","118"],["dc.bibliographiccitation.volume","501"],["dc.contributor.author","Nguyen, Tran Tuan"],["dc.contributor.author","Spehr, Jens"],["dc.contributor.author","Sitzmann, Jonas"],["dc.contributor.author","Baum, Marcus"],["dc.contributor.author","Zug, Sebastian"],["dc.contributor.author","Kruse, Rudolf"],["dc.contributor.editor","Lee, S."],["dc.contributor.editor","Ko, H."],["dc.contributor.editor","Oh, S."],["dc.date.accessioned","2019-07-29T12:25:38Z"],["dc.date.available","2019-07-29T12:25:38Z"],["dc.date.issued","2018"],["dc.description.abstract","This paper presents a framework for robust lane detection towards automated driving using multiple sensors. Since every single source (e.g., camera, digital map, etc.) can fail in certain situations, several independent sources need to be combined. Moreover, the reliability of each source strongly depends on environmental conditions, e.g., existence or visibility of lane markings. Thus, we introduce a concept of estimating and incorporating reliability into the fusion. First, a new sensor-independent error metric is applied to assess the quality of the estimated ego-lanes based on the angle deviation. Secondly, we deploy a boosting algorithm to select the highly discriminant features among the extracted information. Based on the selected features, we apply different classifiers to learn the reliabilities of the sources. Thirdly, we use Dempster-Shafer evidence theory to stabilize the estimated reliabilities over time. Using a big collection of real data recordings from different situations, the experimental results support our concept."],["dc.identifier.doi","10.1007/978-3-319-90509-9_6"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62138"],["dc.language.iso","en"],["dc.publisher","Springer"],["dc.publisher.place","Cham"],["dc.relation.crisseries","Lecture Notes in Electrical Engineering"],["dc.relation.isbn","978-3-319-90508-2"],["dc.relation.isbn","978-3-319-90509-9"],["dc.relation.ispartof","Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System"],["dc.relation.ispartofseries","Lecture Notes in Electrical Engineering;"],["dc.relation.issn","1876-1100"],["dc.relation.issn","1876-1119"],["dc.title","Improving Ego-Lane Detection by Incorporating Source Reliability"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","239"],["dc.bibliographiccitation.lastpage","252"],["dc.bibliographiccitation.volume","501"],["dc.contributor.author","Sigges, Fabian"],["dc.contributor.author","Baum, Marcus"],["dc.contributor.editor","Lee, S"],["dc.contributor.editor","Koh, H."],["dc.contributor.editor","Oh, S."],["dc.date.accessioned","2019-07-29T12:28:01Z"],["dc.date.available","2019-07-29T12:28:01Z"],["dc.date.issued","2018"],["dc.description.abstract","In this chapter, we present an approach to Multi-Object Tracking (MOT) that is based on the Ensemble Kalman Filter (EnKF). The EnKF is a standard algorithm for data assimilation in high-dimensional state spaces that is mainly used in geosciences, but has so far only attracted little attention for object tracking problems. In our approach, the Optimal Subpattern Assignment (OSPA) distance is used for coping with unlabeled noisy measurements and a robust covariance estimation is done using FastMCD to deal with possible outliers due to false detections. A simple gating technique allows handling of missing detections. Additionally, a recently proposed JPDA variant of the EnKF is discussed. The filters are evaluated in two different scenarios with false detections, where a nearest neighbour Kalman Filter (NN-KF) serves as a baseline."],["dc.identifier.doi","10.1007/978-3-319-90509-9_14"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62139"],["dc.language.iso","en"],["dc.publisher","Springer"],["dc.publisher.place","Cham"],["dc.relation.crisseries","Lecture Notes in Electrical Engineering"],["dc.relation.isbn","978-3-319-90508-2"],["dc.relation.isbn","978-3-319-90509-9"],["dc.relation.ispartof","Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System"],["dc.relation.ispartofseries","Lecture Notes in Electrical Engineering;"],["dc.relation.issn","1876-1100"],["dc.relation.issn","1876-1119"],["dc.title","Ensemble Kalman Filter Variants for Multi-Object Tracking with False and Missing Measurements"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]