To Örebro University

oru.seÖrebro University Publications
Change search
Refine search result
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Bennewitz, Maren
    et al.
    University of Freiburg.
    Burgard, Wolfram
    University of Freiburg.
    Cielniak, Grzegorz
    Örebro University, Department of Technology.
    Thrun, Sebastian
    Carnegie Mellon University.
    Learning motion patterns of people for compliant robot motion2005In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 24, no 1, p. 31-48Article in journal (Refereed)
    Abstract [en]

    Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns enables a mobile robot to robustly keep track of persons in its environment and to improve its behavior. This paper proposes a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders is clustered using the expectation maximization algorithm. Based on the result of the clustering process we derive a Hidden Markov Model (HMM) that is applied to estimate the current and future positions of persons based on sensory input. We also describe how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process. We present several experiments carried out in different environments with a mobile robot equipped with a laser range scanner and a camera system. The results demonstrate that our approach can reliably learn motion patterns of persons, can robustly estimate and predict positions of persons, and can be used to improve the navigation behavior of a mobile robot.

  • 2.
    Cielniak, Grzegorz
    Örebro University, Department of Technology.
    People tracking by mobile robots using thermal and colour vision2007Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis addresses the problem of people detection and tracking by mobile robots in indoor environments. A system that can detect and recognise people is an essential part of any mobile robot that is designed to operate in populated environments. Information about the presence and location of persons in the robot’s surroundings is necessary to enable interaction with the human operator, and also for ensuring the safety of people near the robot.

    The presented people tracking system uses a combination of thermal and colour information to robustly track persons. The use of a thermal camera simplifies the detection problem, which is especially difficult on a mobile platform. The system is based on a fast and efficient samplebased tracking method that enables tracking of people in real-time. The elliptic measurement model is fast to calculate and allows detection and tracking of persons under different views. An explicit model of the human silhouette effectively distinguishes persons from other objects in the scene. Moreover the process of detection and localisation is performed simultaneously so that measurements are incorporated directly into the tracking framework without thresholding of observations. With this approach persons can be detected independently from current light conditions and in situations where other popular detection methods based on skin colour would fail.

    A very challenging situation for a tracking system occurs when multiple persons are present on the scene. The tracking system has to estimate the number and position of all persons in the vicinity of the robot. Tracking of multiple persons in the presented system is realised by an efficient algorithm that mitigates the problems of combinatorial explosion common to other known algorithms. A sequential detector initialises an independent tracking filter for each new person appearing in the image. A single filter is automatically deleted when it stops tracking a person. While thermal vision is good for detecting people, it can be very difficult to maintain the correct association between different observations and persons, especially where they occlude one another, due to the unpredictable appearance and social behaviour of humans. To address these problems the presented tracking system uses additional information from the colour camera. An adaptive colour model is incorporated into the measurement model of the tracker to improve data association. For this purpose an efficient integral image based method is used to maintain the real-time performance of the tracker. To deal with occlusions the system uses an explicit method that first detects situations where people occlude each other. This is realised by a new approach based on a machine learning classifier for pairwise comparison of persons that uses both thermal and colour features provided by the tracker. This information is then incorporated into the tracker for occlusion handling and to resolve situations where persons reappear in a scene.

    Finally the thesis presents a comprehensive, quantitative evaluation of the whole system and its different components using a set of well defined performance measures. The behaviour of the system was investigated on different data sets including different real office environments and different appearances and behaviours of persons. Moreover the influence of all important system parameters on the performance of the system was checked and their values optimised based on these results.

    Download full text (pdf)
    FULLTEXT01
  • 3.
    Cielniak, Grzegorz
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    People recognition by mobile robots2004In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 15, no 1, p. 21-27Article in journal (Refereed)
    Abstract [en]

    This paper addresses the problem of detecting and identifying persons with a mobile robot, by sensory fusion of thermal and colour vision information. In the proposed system, people are first detected with a thermal camera, using image analysis techniques to segment the persons in the thermal images. This information is then used to segment the corresponding regions of the colour images, using an affine transformation to solve the image correspondence between the two cameras. After segmentation, the region of the image containing a person is further divided into regions corresponding to the person's head, torso and legs. Temperature and colour features are then extracted from each region for input to a pattern recognition system. Three alternative classfication methods were investigated in experiments with a moving mobile robot and moving persons in an office environment. The best identification performance was obtained with a dynamic recognition method based on a Bayes classifier, which takes into account evidence accumulated in a sequence of images.

  • 4.
    Cielniak, Grzegorz
    et al.
    Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom.
    Duckett, Tom
    Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Improved data association and occlusion handling for vision-based people tracking by mobile robots2007In: 2007 IEEE/RSJ international conference on intelligent robots and systems, New York, NY, USA: IEEE, 2007, p. 3436-3441Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach for tracking multiple persons using a combination of colour and thermal vision sensors on a mobile robot. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is then incorporated into the tracker.

    Download full text (pdf)
    Improved Data Association and Occlusion Handling for Vision-Based People Tracking by Mobile Robots
    Download full text (txt)
    Publiceringsmedgivande
  • 5.
    Cielniak, Grzegorz
    et al.
    Örebro University, Department of Technology.
    Miladinovic, Mihajlo
    Dept. of Technology, AASS, Örebro University, Örebro, Sweden.
    Hammarin, Daniel
    Dept. of Technology, AASS, Örebro University, Örebro, Sweden.
    Göransson, Linus
    Dept. of Technology, AASS, Örebro University, Örebro, Sweden.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Appearance-based tracking of persons with an omnidirectional vision sensor2003In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, 2003, Vol. 7, article id 4624346Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of tracking a moving person with a single, omnidirectional camera. An appearance-based tracking system is described which uses a self-acquired appearance model and a Kalman filter to estimate the position of the person. Features corresponding to ``depth cues'' are first extracted from the panoramic images, then an artificial neural network is trained to estimate the distance of the person from the camera. The estimates are combined using a discrete Kalman filter to track the position of the person over time. The ground truth information required for training the neural network and the experimental analysis was obtained from another vision system, which uses multiple webcams and triangulation to calculate the true position of the person. Experimental results show that the tracking system is accurate and reliable, and that its performance can be further improved by learning multiple, person-specific appearance models

    Download full text (pdf)
    Appearance-based Tracking of Persons with an Omnidirectional Vision Sensor
  • 6.
    Cielniak, Grzegorz
    et al.
    Örebro University, Department of Technology.
    Treptow, André
    University of Tübingen.
    Duckett, Tom
    Örebro University, Department of Technology.
    Quantitative performance evaluation of a people tracking system on a mobile robot2005Conference paper (Refereed)
    Abstract [en]

    Future service robots will need to keep track of the persons in their environment. A number of people tracking systems have been developed for mobile robots, but it is currently impossible to make objective comparisons of their performance. This paper presents a comprehensive, quantitative evaluation of a state-of-the-art people tracking system for a mobile robot in an office environment, for both single and multiple persons.

  • 7.
    Treptow, André
    et al.
    University of Tubingen.
    Cielniak, Grzegorz
    Örebro University, Department of Technology. AASS.
    Duckett, Tom
    Örebro University, Department of Technology.
    Active people recognition using thermal and grey images on a mobile security robot2005In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005): IROS 2005 IEEE/RSJ -, 2005, p. 2103-2108Conference paper (Refereed)
    Abstract [en]

    In this paper we present a vision-based approach to detect, track and identify people on a mobile robot in real time. While most vision systems for tracking people on mobile robots use skin color information, we present an approach using thermal images and a fast contour model together with a Particle Filter. With this method a person can be detected independently from current light conditions and in situations were no skin color is visible (the person is not close or does not face the robot). Tracking in thermal images is used as an attention system to get an estimate of the position of a person. Based on this estimate we use a pan-tilt camera to zoom to the expected face region and apply a fast face tracker in combination with face recognition to identify the person.

  • 8.
    Treptow, André
    et al.
    University of Tubingen.
    Cielniak, Grzegorz
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Comparing measurement models for tracking people in thermal images on a mobile robot2005Conference paper (Refereed)
    Abstract [en]

    While most vision systems for tracking people on mobile robots use skin color information, we present an approach using thermal images and two different measurement models together with a Particle Filter. With this method a person can be detected independently from current light conditions and in situations were no skin color is visible (the person is not close or does not face the robot). The results show that a measurement model that was learned from local greyscale features improved on the performance of an elliptic contour model, and that both models could be used in combination to further improve performance with minimal extra computational cost

  • 9.
    Treptow, André
    et al.
    University of T ubingen.
    Cielniak, Grzegorz
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln.
    Real-time people tracking for mobile robots using thermal vision2006In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 54, no 9, p. 729-739Article in journal (Refereed)
    Abstract [en]

    This paper presents a vision-based approach for tracking people on a mobile robot using thermal images. The approach combines a particle filter with two alternative measurement models that are suitable for real-time tracking. With this approach a person can be detected independently from current light conditions and in situations where no skin colour is visible. In addition, the paper presents a comprehensive, quantitative evaluation of the different methods on a mobile robot in an office environment, for both single and multiple persons. The results show that the measurement model that was learned from local greyscale features could improve on the performance of the elliptic contour model, and that both models could be combined to further improve performance with minimal extra computational cost.

1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf