- AutorIn
- Peter Poschmann
- Titel
- Multi-sensor multi-person tracking on a mobile robot platform
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:105-qucosa-235210
- Datum der Einreichung
- 06.06.2017
- Datum der Verteidigung
- 02.01.2018
- Abstract (EN)
- Service robots need to be aware of persons in their vicinity in order to interact with them. People tracking enables the robot to perceive persons by fusing the information of several sensors. Most robots rely on laser range scanners and RGB cameras for this task. The thesis focuses on the detection and tracking of heads. This allows the robot to establish eye contact, which makes interactions feel more natural. Developing a fast and reliable pose-invariant head detector is challenging. The head detector that is proposed in this thesis works well on frontal heads, but is not fully pose-invariant. This thesis further explores adaptive tracking to keep track of heads that do not face the robot. Finally, head detector and adaptive tracker are combined within a new people tracking framework and experiments show its effectiveness compared to a state-of-the-art system.
- Freie Schlagwörter (DE)
- Personentracking, Kopftracking, Adaptives Tracking, Kopf Detektion, Autonome Service-Roboter
- Freie Schlagwörter (EN)
- People Tracking, Head Tracking, Adaptive Tracking, Head Detection, Autonomous Service Robot
- Klassifikation (DDC)
- 004
- Normschlagwörter (GND)
- Gesichtserkennung, Objektverfolgung, Serviceroboter
- GutachterIn
- Prof. Dr.-Ing. Bernhard Jung
- Prof. Dr.-Ing. habil. Hans-Joachim Böhme
- Prof. Dr. Horst-Michael Groß
- BetreuerIn
- Prof. Dr.-Ing. Bernhard Jung
- Prof. Dr.-Ing. habil. Hans-Joachim Böhme
- Den akademischen Grad verleihende / prüfende Institution
- TU Bergakademie Freiberg, Freiberg
- URN Qucosa
- urn:nbn:de:bsz:105-qucosa-235210
- Veröffentlichungsdatum Qucosa
- 28.05.2018
- Dokumenttyp
- Dissertation
- Sprache des Dokumentes
- Englisch