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Edgar Seemann
Researcher at Technische Universität Darmstadt
Publications - 6
Citations - 1609
Edgar Seemann is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Object detection & Pedestrian detection. The author has an hindex of 6, co-authored 6 publications receiving 1526 citations.
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Proceedings ArticleDOI
Pedestrian detection in crowded scenes
TL;DR: Qualitative and quantitative results on a large data set confirm that the core part of the method is the combination of local and global cues via probabilistic top-down segmentation that allows examining and comparing object hypotheses with high precision down to the pixel level.
Book ChapterDOI
The 2005 PASCAL visual object classes challenge
Mark Everingham,Andrew Zisserman,Christopher Williams,Luc Van Gool,Moray Allan,Christopher M. Bishop,Olivier Chapelle,Navneet Dalal,Thomas Deselaers,Gyuri Dorkó,Stefan Duffner,J Eichhorn,Jason Farquhar,Mario Fritz,Christophe Garcia,Tom Griffiths,Frédéric Jurie,Daniel Keysers,Markus Koskela,Jorma Laaksonen,Diane Larlus,Bastian Leibe,Hongying Meng,Hermann Ney,Bernt Schiele,Cordelia Schmid,Edgar Seemann,John Shawe-Taylor,Amos Storkey,Sandor Szedmak,Bill Triggs,Ilkay Ulusoy,Ville Viitaniemi,Jianguo Zhang +33 more
TL;DR: The PASCAL Visual Object Classes Challenge (PASCALVOC) as mentioned in this paper was held from February to March 2005 to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects).
Proceedings ArticleDOI
Multi-Aspect Detection of Articulated Objects
TL;DR: An important property of this new approach is to share local appearance across different articulations and viewpoints, therefore requiring relatively few training samples, and the effectiveness of the approach is shown and compared to previous approaches.
Proceedings ArticleDOI
An Evaluation of Local Shape‐based Features for Pedestrian Detection
TL;DR: Shape Context trained on real edge images rather than on clean pedestrian silhouettes combined with the Hessian-Laplace detector outperforms all other tested approaches for the detection of pedestrians.
Proceedings ArticleDOI
Towards Robust Pedestrian Detection in Crowded Image Sequences
TL;DR: This work presents a generative object model that is capable to scale from a general object class model to a more specific object-instance model that allows to detect class instances as well as to distinguish between individual object instances reliably.