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Road Target Tracking with an Approximative Rao-Blackwellized Particle Filter
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2009 (English)In: Proceedings from the 12th International Conference on Information Fusion, 6-9 July, Seattle, Washington, USA, IEEE conference proceedings, 2009, 17-24 p.Conference paper, Published paper (Refereed)
Abstract [en]

Using prior information about the road network will improve the estimation performance for a road constrained target significantly. Several estimation methods have been proposed to handle the multi-modality that arise in a road target tracking application. One popular filter suitable for this kind of non-linear problems is the Particle Filter, but a major drawback is that the Particle filter requires a large amount of particles as the state dimension increases to maintain a good approximation of the filtering distribution. In this paper a Rao-Blackwellized Particle Filter based approach is proposed to reduce the dimension of the state space in road target tracking applications. Furthermore, it is also shown how prior information about the probability of detection can be used to improve the estimation performance further.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2009. 17-24 p.
Keyword [en]
Marginalized particle filter, Probability of detection, Rao-Blackwellized particle filter, Road target tracking
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-52977ISI: 000273560000003ISBN: 978-0-9824438-0-4 (print)OAI: oai:DiVA.org:liu-52977DiVA: diva2:286391
Conference
12th International Conference on Information Fusion, July, Seattle, WA, USA
Available from: 2010-01-14 Created: 2010-01-14 Last updated: 2013-07-06Bibliographically approved
In thesis
1. Tracking and Planning for Surveillance Applications
Open this publication in new window or tab >>Tracking and Planning for Surveillance Applications
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Vision and infrared sensors are very common in surveillance and security applications, and there are numerous examples where a critical infrastructure, e.g. a harbor, an airport, or a military camp, is monitored by video surveillance systems. There is a need for automatic processing of sensor data and intelligent control of the sensor in order to obtain efficient and high performance solutions that can support a human operator. This thesis considers two subparts of the complex sensor fusion system; namely target tracking and sensor control.The multiple target tracking problem using particle filtering is studied. In particular, applications where road constrained targets are tracked with an airborne video or infrared camera are considered. By utilizing the information about the road network map it is possible to enhance the target tracking and prediction performance. A dynamic model suitable for on-road target tracking with a camera is proposed and the computational load of the particle filter is treated by a Rao-Blackwellized particle filter. Moreover, a pedestrian tracking framework is developed and evaluated in a real world experiment. The exploitation of contextual information, such as road network information, is highly desirable not only to enhance the tracking performance, but also for track analysis, anomaly detection and efficient sensor management. Planning for surveillance and reconnaissance is a broad field with numerous problem definitions and applications. Two types of surveillance and reconnaissance problems are considered in this thesis. The first problem is a multi-target search and tracking problem. Here, the task is to control the trajectory of an aerial sensor platform and the pointing direction of its camera to be able to keep track of discovered targets and at the same time search for new ones. The key to successful planning is a measure that makes it possible to compare different tracking and searching tasks in a unified framework and this thesis suggests one such measure. An algorithm based on this measure is developed and simulation results of a multi-target search and tracking scenario in an urban area are given. The second problem is aerial information exploration for single target estimation and area surveillance. In the single target case the problem is to control the trajectory of a sensor platform with a vision or infrared camera such that the estimation performance of the target is maximized. The problem is treated both from an information filtering and from a particle filtering point of view. In area exploration the task is to gather useful image data of the area of interest by controlling the trajectory of the sensor platform and the pointing direction of the camera. Good exploration of a point of interest is characterized by several images from different viewpoints. A method based on multiple information filters is developed and simulation results from area and road exploration scenarios are presented.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 252 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1432
Keyword
target tracking, sensor control, planning, surveillance, vision sensor
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-76883 (URN)978-91-7519-941-2 (ISBN)
Public defence
2012-06-07, Signalen, Hus B, Campus Valla, Linköping universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2012-04-23 Created: 2012-04-23 Last updated: 2012-05-04Bibliographically approved

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Skoglar, PerOrguner, UmutTörnqvist, DavidGustafsson, Fredrik

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