Skip to main content
Article thumbnail
Location of Repository

Combined data association and evolving particle filter for tracking of multiple articulated objects.

By Harish Bhaskar and Lyudmila Mihaylova

Abstract

This paper proposes an approach for tracking multiple articulated targets using a combined data association and evolving population particle filter. A visual target is represented as a pictorial structure using a collection of parts together with a model of their geometry. Tracking multiple targets in video involves an iterative alternating scheme of selecting valid measurements belonging to a target from a clutter or other measurements that all fall within a validation gate. An algorithm with extended likelihood probabilistic data association and evolving groups of populations of particles representing a multiple-part distribution is designed. Variety in the particles is introduced using constrained genetic operators both in the sampling and resampling steps. We explore the effect of various model parameters on system performance and show that the proposed model achieves better accuracy than other widely used methods on standard datasets

Topics: AI Indexes (General), QA75 Electronic computers. Computer science, TA Engineering (General). Civil engineering (General)
Year: 2011
DOI identifier: 10.1155/2011
OAI identifier: oai:eprints.lancs.ac.uk:39961
Provided by: Lancaster E-Prints

Suggested articles

Citations

  1. (2006). A .Y i l m a z ,O .J a v e d ,a n dM .S h a h ,“ O b j e c tt r a c k i n g :as u r v e y ,
  2. (2006). A data association algorithm for multiple object tracking in video sequences,” doi
  3. (1999). A g g arwalan dQ.C ai ,“ H u manmot i onan al y si s:ar evi e w ,
  4. (2003). A Rao-Blackwellised unscented Kalman filter,”
  5. (2001). A survey of computer visionbased human motion capture,” doi
  6. (2003). Adaptive visual tracking and recognition using particle filters,” doi
  7. (2002). Bar-Shalom,“Expected likelihood for tracking in clutter with particle filters,” doi
  8. (2004). Beyond the Kalman Filter: Particle Filter for Tracking Applications, doi
  9. (2004). Beyond the Kalman Filter: ParticleFilter for Tracking Applications, Artech House,
  10. CAVIAR test case scenarios, 2005,http://homepages.inf.ed.ac .uk/rbf/.
  11. (2005). Comparison of resampling schemes for particle filtering,” doi
  12. (2006). Computational studies of human motion: part 1, tracking and motion synthesis,” Foundations and Trends doi
  13. (1999). Design and Analysis of Modern Tracking Systems, Artech House Radar Library,
  14. (2003). Detecting moving objects, ghosts, and shadows in video streams,” doi
  15. (2003). Efficient particle filtering for multiple target tracking with application to tracking in structured images,” doi
  16. (2003). Efficient particle filtering for multiple target tracking with applicationtotrackinginstructured images,”ImageandVision doi
  17. (2002). Engineer’s guide to variable-structure multiplemodel estimation for tracking,” in Multitarget-Multisensor Tracking: Applications
  18. (1993). Estimation and Tracking: Principles,Techniques and Software, Artech House, doi
  19. (2002). Expected likelihood for tracking in clutter with particle filters,” doi
  20. (2006). F o r s y t h ,O .A r i k a n ,L .I k e m o t o ,J .O ’ B r i e n ,a n dD . Ramanan, “Computational studies of human motion: part 1, tracking and motion synthesis,” Foundations and Trends doi
  21. (2004). Fast mutual exclusion,” doi
  22. (2003). Finding and tracking people from the bottom up,” doi
  23. (2001). Granum, “A survey of computer visionbased human motion capture,”
  24. (1999). Human motion analysis: a review,” doi
  25. (2006). J.Hol,T.Sh¨ on,andF.Gustaffsson,“Onresamplingalgorithms for particlefilters,”
  26. (2010). L´ o p e z ,A .D .S a p p a ,a n dT .G r a f , “Survey of pedestrian detection for advanced driver assistance systems,”
  27. (2006). Multiple objectstracking using particle filters in video sequences,” doi
  28. (1994). Multisensor multitarget mixture reduction algorithms for tracking,” doi
  29. (2000). Multitarget-Multisensor Tracking: Applications and Advances,
  30. (2000). Multitarget-Multisensor Tracking: Applications and Advances,v o l .3 ,A r t e c hH o u s e ,
  31. (2008). n d r i l u k a ,S .R o t h ,a n dB .S c h i e l e ,“ P e o p l e - t r a c k i n g - b y -detection and people-detection-by-tracking,” doi
  32. (2006). Object tracking: a survey,” doi
  33. (2007). On populationbased simulation for static inference,” doi
  34. (2006). On resampling algorithms for particlefilters,”
  35. (2004). Particle Filter for Tracking Applications,v o l .2 ,A r t e c h House,
  36. (2004). ParticleFilter for Tracking Applications,A r t e c hH o u s e ,
  37. (2007). Pedestrian protection systems: issues, survey, and challenges,” doi
  38. (2007). Pedestrian protection systems: issues,survey, andchallenges,” doi
  39. (2003). People tracking with anonimous and ID-sensors using Rao-Blackwellised particle filters,”
  40. (2008). People-tracking-bydetection and people-detection-by-tracking,” doi
  41. (2005). Pictorial structures for object recognition,”
  42. (2004). Population monte carlo,” doi
  43. (2000). Population-based monte carlo algorithms,”
  44. (2000). Population-based monte carlo algorithms,”Journal of
  45. (2004). Probabilistic data association techniques for target tracking in clutter,” doi
  46. (2006). Real-time tracking of hundreds of targets with efficient exact JPDAF implementation,” in doi
  47. (2010). Survey of pedestrian detection for advanced driver assistance systems,” doi
  48. (2003). The over-extended Kalman filter— use it!,”
  49. (2001). The unscented Kalman filter ,” in Kalman Filtering and Neural Networks, doi
  50. (2001). The unscented Kalman filter ,” in Kalman Filtering and Neural Networks,S .H a y k i n doi
  51. (1999). The visual analysis of human movement: a survey,” doi
  52. (1997). Tracking and identification forclosely spaced objects in clutter,” doi
  53. (1997). Tracking and motion estimation of the articulated object: a hierarchical Kalman filter approach,” doi
  54. (2007). Tracking people by learning their appearance,” doi
  55. (2003). Two-dimensional assignment with merged measurements using Lagrangian relaxation,” doi

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