Skip to main content
Article thumbnail
Location of Repository

Data association and occlusion handling for vision-based people tracking by mobile robots

By Grzegorz Cielniak, Tom Duckett and Achim J. Lilienthal


This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. 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 incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets

Topics: H670 Robotics and Cybernetics, G400 Computer Science, H671 Robotics, G740 Computer Vision
Publisher: Elsevier B.V.
Year: 2010
DOI identifier: 10.1016/j.robot.2010.02.004
OAI identifier:

Suggested articles


  1. (2004). A boosted particle Multitarget detection and tracking, in: doi
  2. (1995). A decision-theoretic generalization of on-line learning and an application to boosting, in: doi
  3. (2008). A mobile vision system for robust multi-person tracking, in: doi
  4. (1979). An algorithm for tracking multiple targets, in: Proc. doi
  5. (2004). An MCMC-based particle for tracking multiple interacting targets, in: doi
  6. (2001). Appearance models for occlusion handling, in: doi
  7. (1999). Bayesian Multiple Target Tracking, Artech House,
  8. (2004). Beyond the Kalman Filter -Particle Filters for Tracking Applications, Artech House,
  9. (2001). Bramble: A Bayesian multiple-blob tracker., in: doi
  10. (1998). Condensation { conditional density propagation for visual tracking doi
  11. (2007). Development of ALACRANE: A mobile robotic assistance for exploration and rescue missions, in: doi
  12. (2005). Evaluating multi-object tracking, in: Workshop on Empirical Evaluation Methods in Computer Vision, doi
  13. (2004). Face tracking and hand gesture recognition for human-robot interaction, in: doi
  14. (2007). Garc a-Silvente, People detection and tracking using stereo vision and color, doi
  15. (2005). Kr ose, Keeping track of humans: Have i seen this person before?, in: doi
  16. (2002). M2tracker: A multi-view approach to segmenting and tracking people in a cluttered scene using region-based stereo, in: doi
  17. (2009). Mobile Robot Navigation with Intelligent Infrared Image Interpretation, doi
  18. (2001). Monte Carlo data association for multiple target tracking, in: doi
  19. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation, doi
  20. (1997). P Realtime tracking of the human body, doi
  21. (2003). Pedestrian detection in infrared images, in: doi
  22. (2007). People tracking by mobile robots using thermal and colour vision, doi
  23. (2001). Probabilistic framework for segmenting people under occlusion, in: doi
  24. (2005). Probabilistic object tracking based on machine learning and importance sampling, in: doi
  25. (2002). Probabilistic template based pedestrian detection in infrared videos, in: doi
  26. (2001). Rapid object detection using a boosted cascade of simple features, in: doi
  27. (1997). Real-time closed-world tracking, in: doi
  28. robot league team: Darmstadt rescue robot team (germany),
  29. (2002). Sensor fusion for vision and sonar based people tracking on a mobile service robot, in:
  30. (2000). Tools and techniques for video performance evaluation, in: doi
  31. (1988). Tracking and Data Association, doi
  32. (2000). Tracking groups of people 1 (80) doi
  33. (2001). Tracking multiple moving objects with a mobile robot, in: doi
  34. (2000). Tracking people in presence of occlusion, in:

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.