Shop : Details
Shop
Details
Dezember 2012
Eric Richter
Non-Parametric Bayesian Filtering for Multiple Object Tracking
Advanced driver assistance systems increase the comfort, efficiency, and safety of nowadays and future automobiles. Especially if these systems need to derive a safety critical decision like an emergency brake they require a reliable and precise environment recognition in order to keep the false triggering rate close to zero.
In this work, environment recognition means to recursively estimate both the time varying number of objects in a scene and their parameters like position and velocity—so called multiple object tracking. The thesis summarizes typical state of the art multiple object tracking approaches which classically consist of separate detection, observation association, and estimation stages. Often, the detection and association steps derive decisions which are hardly reversible during the tracking process. Additionally, the majority of current multiple object tracking systems insufficiently model the spatial extension of objects though high resolution sensors like laser scanner can observe it.
The scope of this work is to overcome these limitations by integrating dynamic as well as a priori knowledge into one Bayes filter, which is implemented by a reversible jump Markov chain Monte Carlo sampling approach. By that, it is possible to track spatially extended objects without dedicated detection and association steps. Instead, several models are combined in an integrated Bayesian estimation process. These models include how objects look like and move, where they are expected to appear and disappear, and how they interact with each other. By that, the approach contributes to the field of spatially extended object tracking and provides many connection points for further investigation.
The resulting multiple object tracking system rigorously utilizes the Bayesian framework to cope with the uncertainties occurring in different domains. This includes association ambiguities as well as observation and system process noises. Furthermore, a track management is included in a statistical fashion.
The work demonstrates three case studies of multiple spatially extended object tracking utilizing different sensors and algorithmic approaches. At first, a data fusion system combining a radar and a camera sensor using a classical multiple object tracking method is shown. Hereafter, a lidar based system is demonstrated which uses advanced occupancy grid methods in order to detect and track spatially extended objects. Finally, an implementation of the reversible jump Markov chain Monte Carlo sampling approach for a lidar based tracking of spatially extended objects is shown.
In this work, environment recognition means to recursively estimate both the time varying number of objects in a scene and their parameters like position and velocity—so called multiple object tracking. The thesis summarizes typical state of the art multiple object tracking approaches which classically consist of separate detection, observation association, and estimation stages. Often, the detection and association steps derive decisions which are hardly reversible during the tracking process. Additionally, the majority of current multiple object tracking systems insufficiently model the spatial extension of objects though high resolution sensors like laser scanner can observe it.
The scope of this work is to overcome these limitations by integrating dynamic as well as a priori knowledge into one Bayes filter, which is implemented by a reversible jump Markov chain Monte Carlo sampling approach. By that, it is possible to track spatially extended objects without dedicated detection and association steps. Instead, several models are combined in an integrated Bayesian estimation process. These models include how objects look like and move, where they are expected to appear and disappear, and how they interact with each other. By that, the approach contributes to the field of spatially extended object tracking and provides many connection points for further investigation.
The resulting multiple object tracking system rigorously utilizes the Bayesian framework to cope with the uncertainties occurring in different domains. This includes association ambiguities as well as observation and system process noises. Furthermore, a track management is included in a statistical fashion.
The work demonstrates three case studies of multiple spatially extended object tracking utilizing different sensors and algorithmic approaches. At first, a data fusion system combining a radar and a camera sensor using a classical multiple object tracking method is shown. Hereafter, a lidar based system is demonstrated which uses advanced occupancy grid methods in order to detect and track spatially extended objects. Finally, an implementation of the reversible jump Markov chain Monte Carlo sampling approach for a lidar based tracking of spatially extended objects is shown.
Schlagwörter: Spatially Extended Object Tracking; Data Association; Reversible Jump Markov Chain Monte Carlo; Advanced Driver Assistance Systems; Data Fusion
Forschungsberichte der Professur Nachrichtentechnik
Herausgegeben von Prof. Dr.-Ing. Gerd Wanielik, Chemnitz
Band 8
Verfügbare Online-Dokumente zu diesem Titel
DOI 10.2370/9783844014884
Sie benötigen den Adobe Reader, um diese Dateien ansehen zu können. Hier erhalten Sie eine kleine Hilfe und Informationen, zum Download der PDF-Dateien.
Bitte beachten Sie, dass die Online-Dokumente nicht ausdruckbar und nicht editierbar sind.
Bitte beachten Sie auch weitere Informationen unter: Hilfe und Informationen.
Bitte beachten Sie auch weitere Informationen unter: Hilfe und Informationen.
| Dokument |  | Abstract / Kurzzusammenfassung | ||
| Dateiart |  | |||
| Kosten |  | frei | ||
| Aktion |  | Download der Datei | ||
| Dokument |  | Gesamtdokument | ||
| Dateiart | | |||
| Kosten |  | 36,60 € | ||
| Aktion |  | Zahlungspflichtig kaufen und download der Datei | ||
| Dokument |  | Inhaltsverzeichnis | ||
| Dateiart | | |||
| Kosten | | frei | ||
| Aktion | | Download der Datei | ||
Benutzereinstellungen für registrierte Online-Kunden (Online-Dokumente)
Sie können hier Ihre Adressdaten ändern sowie bereits georderte Dokumente erneut aufrufen.
Benutzer
Nicht angemeldet
Export bibliographischer Daten
Shaker Verlag GmbHAm Langen Graben 15a52353 Düren
Mo. - Do. 8:00 Uhr bis 16:00 UhrFr. 8:00 Uhr bis 15:00 Uhr
Kontaktieren Sie uns. Wir helfen Ihnen gerne weiter.
