Biehl, Robert (2013)
Part based object detection with a flexible context constraint.
Technische Universität Darmstadt
Diplom- oder Magisterarbeit, Erstveröffentlichung
Kurzbeschreibung (Abstract)
This work describes an object detection system which integrates flexible spatial context constraints to improve detection performance. It allows spatial and scale deformation of the object relative to its context. The contextual model extends an existing deformable parts model and is trained on partially labeled data using a latent SVM. The approach can be applied to any object detection problem where the object class always exists in one typical image context, but the context can appear independently. A new scoring method is used to model the asymmetric relationship between object and context. Furthermore, the system enables the use of contextual non-maximum suppression, a context sensitive way to discard redundant detections. Trained on our combined dataset of dresses and persons, the system achieves a significant improvement in detection performance when compared with basic deformable parts models.
| Typ des Eintrags: | Diplom- oder Magisterarbeit |
|---|---|
| Erschienen: | 2013 |
| Autor(en): | Biehl, Robert |
| Art des Eintrags: | Erstveröffentlichung |
| Titel: | Part based object detection with a flexible context constraint |
| Sprache: | Englisch |
| Referenten: | Roth, Prof. Stefan ; Franzel, M. Sc. Thorsten |
| Publikationsjahr: | 1 Juli 2013 |
| Ort: | Darmstadt |
| Datum der mündlichen Prüfung: | 8 Juni 2013 |
| URL / URN: | http://tuprints.ulb.tu-darmstadt.de/6868 |
| Kurzbeschreibung (Abstract): | This work describes an object detection system which integrates flexible spatial context constraints to improve detection performance. It allows spatial and scale deformation of the object relative to its context. The contextual model extends an existing deformable parts model and is trained on partially labeled data using a latent SVM. The approach can be applied to any object detection problem where the object class always exists in one typical image context, but the context can appear independently. A new scoring method is used to model the asymmetric relationship between object and context. Furthermore, the system enables the use of contextual non-maximum suppression, a context sensitive way to discard redundant detections. Trained on our combined dataset of dresses and persons, the system achieves a significant improvement in detection performance when compared with basic deformable parts models. |
| URN: | urn:nbn:de:tuda-tuprints-68687 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik |
| Hinterlegungsdatum: | 22 Okt 2017 19:55 |
| Letzte Änderung: | 22 Okt 2017 19:55 |
| PPN: | |
| Referenten: | Roth, Prof. Stefan ; Franzel, M. Sc. Thorsten |
| Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: | 8 Juni 2013 |
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