Stereo vision tracking of multiple objects in complex indoor environments

Sensors

Volumn 10, Issue 10, 2010, Pages 8865-8887

  Marrón Romera, Marta ^a   García, Juan C ^a   Sotelo, Miguel A ^a   Pizarro, Daniel ^a   Mazo, Manuel ^a   Cañas, José M ^b   Losada, Cristina ^a   Marcos, Álvaro ^a  

^a UNIVERSITY OF ALCALÁ   (Spain)

^b UNIVERSIDAD REY JUAN CARLOS   (Spain)

Author keywords

3D tracking; Bayesian estimation; Mobile robots; Stereo vision sensor

Indexed keywords

3D TRACKING; BAYESIAN ALGORITHMS; BAYESIAN ESTIMATIONS; CLUSTERING PROCESS; INDOOR ENVIRONMENT; POSITION ESTIMATION; PROBABILISTIC ALGORITHM; STEREO VISION SENSOR;

BAYESIAN NETWORKS; CLUSTERING ALGORITHMS; MOBILE ROBOTS; ROBOT APPLICATIONS;

STEREO VISION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; CLUSTER ANALYSIS; ENVIRONMENT; METHODOLOGY; ROBOTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLUSTER ANALYSIS; ENVIRONMENT; PATTERN RECOGNITION, AUTOMATED; ROBOTICS;

EID: 77957992135     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s101008865     Document Type: Article

References (30)

1. Jia, Z.; Balasuriya, A.; Challa, S. Autonomous vehicles navigation with visual target tracking: Technical approaches. Algorithms 2008, 1, 153-182.
2. Khan, Z.; Balch, T.; Dellaert, F. A Rao-Blackwellized particle filter for eigen tracking. In Proceedings of the Third IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, June 2004; pp. 980-986.
3. Isard, M.; Blake, A. Icondensation: Unifying low-level and high-level tracking in a stochastic framework. In Proceedings of the Fifth European Conference on Computer Vision, Freiburg, Germany, June 1998; Volume 1, pp. 893-908.
4. Chen, Y.; Huang, T.S.; Rui, Y. Mode-based multi-hypothesis head tracking using parametric contours. In Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, USA, May 2002.
5. Odobez, J.M.; Gatica-Perez. D. Embedding motion model-based stochastic tracking. In Proceedings of the Seventeenth International Conference on Pattern Recognition, Cambridge, UK, August 2004; Volume 2, pp. 815-818.
6. Okuma, K.; Taleghani, A.; De Freitas, N.; Little, J.J.; Lowe, D.G. A boosted particle filter: Multi-target detection and tracking. In Proceedings of the Eighth European Conference on Computer Vision, Prague, Czech Republic, May 2004; Volume 3021, Part I, pp. 28-39.
7. Thrun, S. Probabilistic algorithms in robotics. AI Mag. 2000, 21, 93-109.
8. Arulampalam, M.S.; Maskell, S.; Gordon, N.; Clapp, T. A tutorial on particle filters for online nonlinear non-gaussian bayesian tracking. IEEE Trans. Signal. Proces. 2002, 50, 174-188.
9. Gordon, N.J.; Salmond, D.J.; Smith A.F.M. Novel approach to nonlinear/non-gaussian bayesian state estimation. IEEE Proc. F 1993, 140, 107-113.
10. Wang, X.; Wang, S.; Ma, J.-J. An improved particle filter for target tracking in sensor systems, Sensors 2007, 7, 144-156.
11. Welch, G.; Bishop, G. An Introduction to the Kalman Filter. Technical Report: TR95-041; ACM SIGGRAPH: Los Angeles, CA, USA, 2001; Available online: http://www.cs.unc.edu/∼tracker/ref/s2001/kalman/ (accesed on 30 June 2010).
12. Reid, D.B. An algorithm for tracking multiple targets. IEEE Trans. Automat. Contr. 1979, 24, 843-854.
13. Tweed, D.; Calway, A. Tracking many objects using subordinated condensation. In Proceedings of the British Machine Vision Conference, Cardiff, UK, October 2002; pp. 283-292.
14. Smith, K.; Gatica-Perez, D.; Odobez, J.M. Using particles to track varying numbers of interacting people. In Proceedings of the Fourth IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, June 2005; pp. 962-969.
15. MacCormick, J.; Blake, A. A probabilistic exclusion principle for tracking multiple objects. In Proceedings of the Seventh IEEE International Conference on Computer Vision, Corfu, Greece, September 1999; pp. 572-578.
16. Schulz, D.; Burgard, W.; Fox, D.; Cremers, A.B. Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. Int. J. Robot. Res. 2003, 22, 99-116.
17. Hue, C.; Le Cadre, J.P.; Pérez, P. A particle filter to track multiple objects. IEEE Trans. Aero. Elec. Sys. 2002, 38, 791-812.
18. Koller-Meier, E.B.; Ade, F. Tracking multiple objects using a condensation algorithm. J. Robot. Auton. Syst. 2001, 34, 93-105.
19. Schulz, D.; Burgard, W.; Fox, D.; Cremers, A.B. People tracking with mobile robots using sample-based joint probabilistic data association filters. Int. J. Robot. Res. 2003, 22, 99-116.
20. Bar-Shalom, Y.; Fortmann, T. Tracking and Data Association; Academic Press: New York, NY, USA, 1988.
21. Burguera, A.; González, Y.; Oliver, G. Sonar semsor models and their application to mobile robot localization. Sensors 2009, 9, 10217-10243.
22. Boufama, B. Reconstruction Tridimensionnelle en Vision par Ordinateur: Cas des Cameras non Etalonnees. Ph.D. Thesis, Institut National Polytechnique de Grenoble: Grenoble, France, 1994.
23. Canny, F.J. A computational approach to edge detection. IEEE Trans. Pattern Anal. 1986, 8, pp. 679-698.
24. Documentation of function cvHoughLines2. Available online: http://opencv.willowgarage.com/documentation/feature_detection.html (accessed on 27 August 2010).
25. Project Open CV. Available online: http://sourceforge.net/projects/opencvlibrary/(accesed on 27 August 2010).
26. Vermaak, J.; Doucet, A.; Perez, P. Maintaining multimodality through mixture tracking. In Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, France, June 2003; pp. 1110-1116.
27. Marrón, M.; Sotelo, M.A.; García, J.C.; Broddfelt, J. Comparing improved versions of 'K-Means' and 'Subtractive' clustering in a tracking applications. In Proceedings of the Eleventh International Workshop on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain, February 2007; pp. 252-255.
28. Bar Shalom, Y.; Li, X.R. Estimation and Tracking Principles Techniques and Software; Artech House: Boston, MA, USA, 1993.
29. MobileRobots. Available online: http://www.mobilerobots.com/Mobile_Robots.aspx (accessed on 27 August 2010).
30. The Player Project. Available online: http://playerstage.sourceforge.net/ (accessed on 27 August 2010).