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Tracking and Planning for Surveillance Applications
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
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 [en]
target tracking, sensor control, planning, surveillance, vision sensor
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-76883ISBN: 978-91-7519-941-2 (print)OAI: oai:DiVA.org:liu-76883DiVA: diva2:517336
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
List of papers
1. Pedestrian Tracking with an Infrared Sensor using Road Network Information
Open this publication in new window or tab >>Pedestrian Tracking with an Infrared Sensor using Road Network Information
2012 (English)In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 1, no 26, 2012a- p.Article in journal (Refereed) Published
Abstract [en]

This article presents a pedestrian tracking methodology using an infrared sensor for surveillance applications. A distinctive feature of this study compared to the existing pedestrian tracking approaches is that the road network information is utilized for performance enhancement. A multiple model particle filter, which uses two different motion models, is designed for enabling the tracking of both road-constrained (on-road) and unconstrained (off-road) targets. The lateral position of the pedestrians on the walkways are taken into account by a specific on-road target model. The overall framework seamlessly integrates the negative information of occlusion events into the algorithm for which the required modifications are discussed. The resulting algorithm is illustrated on real data from a field trial for different scenarios.

Place, publisher, year, edition, pages
Springer, 2012
Keyword
Pedestrian tracking, Infrared sensor, Road network, Particle filter, Multiple model, Occlusion
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-76888 (URN)10.1186/1687-6180-2012-26 (DOI)
Projects
CADICSSecurity LinkExtended Target Tracking
Funder
Swedish Research CouncilSecurity Link
Available from: 2012-04-23 Created: 2012-04-23 Last updated: 2017-12-07
2. Road Target Tracking with an Approximative Rao-Blackwellized Particle Filter
Open this publication in new window or tab >>Road Target Tracking with an Approximative Rao-Blackwellized Particle Filter
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
Keyword
Marginalized particle filter, Probability of detection, Rao-Blackwellized particle filter, Road target tracking
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-52977 (URN)000273560000003 ()978-0-9824438-0-4 (ISBN)
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
3. Simultaneous Camera Orientation Estimation and Road Target Tracking
Open this publication in new window or tab >>Simultaneous Camera Orientation Estimation and Road Target Tracking
2012 (English)Report (Other academic)
Abstract [en]

Airborne surveillance systems equipped with a vision/infrared camera require good knowledge about the position and orientation of the camera for successful tracking of ground targets. In particular, this is essential when incorporating prior information, like road maps, that is expressed relative a global reference system. Usually, it is possible to obtain good positioning with inertial/satellite navigation systems, but estimating the orientation is generally more difficult. It might be possible to use SLAM (Simultaneous Localization and Mapping) or image registration approaches to support the navigation system, but not always since such approaches require stable features in the images. In this paper the problem of simultaneous orientation error estimation and road target tracking is considered by assuming that the target is constrained to a known road network. A particle filter approach is proposed and it is shown that the result of this filter is close to the performance of the ideal case where the orientation error is perfectly known. However, the performance depends on how informative the road path is and in rare cases the orientation error is unobservable.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 7 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3048
Keyword
Camera orientation, Estimation, Road target tracking
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-76890 (URN)LiTH-ISY-R-3048 (ISRN)
Funder
Security LinkSwedish Research Council
Available from: 2012-04-23 Created: 2012-04-23 Last updated: 2016-11-24Bibliographically approved
4. Road Target Search and Tracking with Gimballed Vision Sensor on a UAV
Open this publication in new window or tab >>Road Target Search and Tracking with Gimballed Vision Sensor on a UAV
2012 (English)Report (Other academic)
Abstract [en]

This work considers a sensor management problem where a number of road bounded vehicles are monitored by a UAV with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 33 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3049
Keyword
UAV surveillance, Sensor management, Path planning, Search theory, Road target tracking, Particle filter, Stochastic scheduling, Security and monitoring
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-76891 (URN)LiTH-ISY-R-3049 (ISRN)
Projects
CADICSSecurity LinkExtended Target Tracking
Funder
Swedish Research Council
Available from: 2012-04-23 Created: 2012-04-23 Last updated: 2014-09-19
5. On Information Measures based on Particle Mixture for Optimal Bearings-only Tracking
Open this publication in new window or tab >>On Information Measures based on Particle Mixture for Optimal Bearings-only Tracking
2009 (English)Report (Other academic)
Abstract [en]

In this work we consider a target tracking scenario where a moving observer with a bearings-only sensor is tracking a target. The tracking performance is highly dependent on the trajectory of the sensor platform, and the problem is to determine how it should maneuver for optimal tracking performance.The problem is considered as a stochastic optimal control problem and two sub-optimal control strategies are presented based on the Information filter and the determinant of the information matrix as the optimization objective. Using the determinant of the information matrix as an objective function in the planning problem is equivalent to using differential entropy of the posterior target density when it is Gaussian. For the non-Gaussian case, an approximation of the differential entropy of a density represented by a particle mixture is proposed. Furthermore, a gradient approximation of the differential entropy is derived and used in a stochastic gradient search algorithm applied to the planning problem.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. 17 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2884
Keyword
Bearings-only tracking, Particle filter, Differential entropy
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-55839 (URN)LiTH-ISY-R-2884 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-02Bibliographically approved
6. Information Based Planning for Aerial Exploration
Open this publication in new window or tab >>Information Based Planning for Aerial Exploration
2012 (English)Report (Other academic)
Abstract [en]

Exploration is in this work defined as the task of efficient information gathering of areas, building, roads, etc., by controlling a pan/tilt camera on a sensor platform. Good exploration is characterized by several images from different directions of the areas of interests and that the images can be used to create maps, video mosaics and multi-view imagery for anomaly and change detection. In this paper an aerial exploration framework based on the information filter is presented. The work is inspired by research on optimal trajectory for bearings-only tracking. A number of static grid points represent the area to be explored and the problem is to plan the trajectory of the sensor platform and the pointing direction of the camera to maximize the exploration performance of the grid points.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 19 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3047
Keyword
Sensor platform, Camera, Framework
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-76892 (URN)LiTH-ISY-R-3047 (ISRN)
Available from: 2012-04-23 Created: 2012-04-23 Last updated: 2014-09-19Bibliographically approved

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Citation style
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  • ieee
  • modern-language-association-8th-edition
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  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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