TRACKING OF EXTENDED CROSSING OBJECTS USING THE VITERBI ALGORITHM
Andreas Kräußling, Frank E. Schneider, Dennis Wildermuth
2004
Abstract
Tracking, which means determining the positions of the humans in the surrounding, is one of the goals in the field of mobile robots that operate in populated environments. This paper is concerned with the special problem of tracking expanded objects under such constraints. A solution in form of a Viterbi based algorithm, which can be useful for real–time systems, is presented. Thus a Maximum–a–posteriori (MAP) filtering technique is applied to perform the tracking process. The mathematical background of the algorithm is proposed. The method uses the robots’ sensors in form of laser range finders and a motion and observation model of the objects being tracked. The special problem of the crossing of two expanded objects is considered. The mathematical background for this problem is enlightened and a solution for it in form of a heuristic algorithm is proposed. This algorithm is tested on simulated and real data.
References
- Bar-Shalom, Y. and Tse, E. (1975). Tracking in a cluttered environment with probabilistic data association. Automatica, Volume 11, pp. 451-460.
- Elfes, A., Prassler, E., and Scholz, J. (1999). Tracking people in a railway station during rush hour. In Proc. of the International Conference on Computer Vision (ICVS), pages 13-15, Las Palmas, Spain. Springer Verlag.
- Fod, A., Howard, A., and Mataric, M. J. (2001). Laserbased people tracking. Technical report, Computer Science Department, University of Southern California, Los Angeles, USA.
- Forney, Jr., G. D. (1973). The viterbi algorithm. In Proceedings of the IEEE, volume Volume 61, pages 268-278.
- Fortmann, T. E., Bar-Shalom, Y., and Scheffe, M. (1983). Sonar tracking of multiple targets using joint probabilistic data association. IEEE Journal Of Oceanic Engineering, Volume OE-8(Number 3).
- Gordon, N., Salmond, D., and Smith, A. (1993). A novel approach to nonlinear/non-gaussian bayesian state estimation. IEEE Proceedings F, Volume 140(Number 2):107-113.
- Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME, Journal Basic Engineering, Volume 82:34-45.
- Koch, W. and Stannus, W. (2003). A new application of the em algorithm: Robot-borne extended object tracking. Technical Report Number 66, Research Institute for Communication, Information Processing and Ergonomics of the Research Establishment of Applied Sciences (FGAN), Wachtberg, Germany.
- Kräußling, A., Schneider, F. E., and Wildermuth, D. (2004). Tracking expanded objects using the viterbi algorithm. to be published in the Proceedings of the IEEE Conference on Intelligent Systems, Varna, Bulgaria.
- Mahalanobis, P. C. (1936). On the generalized distance in statistics. In Proc. Natl. Inst. Science, volume Volume 12, pages 49-55, Calcutta, India.
- Maybeck, P. S. (1979). Stochastic Models, Estimation, and Control. Academic Press, New York, San Francisco, London.
- Pitt, M. and Shephard, N. (1999). Filtering via simulation: auxiliary particle filters. Journal of the American Statistical Association, Volume 994(Number 446).
- Pulford, G. W. and Scala, B. L. (1995). Over-the-horizon radar tracking tracking algorithm using the viterbi algorithm - third report to high frequency radar division. Technical report, Cooperative Research Centre for Sensor Signal and Information Processing, University of Melbourne, Australia.
- Quach, T. and Farrooq, M. (1994). Maximum likelihood track formation with the viterbi algorithm. In Proceedings of the 33rd Conference on Decision and Control, Lake Buena Vista, FL, USA.
- Schulz, D., Burgard, W., Fox, D., and Cremers, A. B. (2001). Tracking multiple moving objects with a mobile robot. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, Hawaii, USA.
- Sclaroff, S. and Rosales, R. (1998). Improved tracking of multiple humans with trajectory prediction and occlusion modeling. In IEEE Conference on Computer Vision and Pattern Recognition, Workshop on the interpretation of Visual Motion, Santa Barbara, CA, USA.
- Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and Its Applications. Springer.
- Thrun, S. (1999). Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, (Number 1):21-71.
- Thrun, S., Fox, D., and Burgard, W. (1999). Markov localization for mobile robots in dynamic environments. Artificial Intelligence, (Number 11):391-427.
- Torrieri, D. J. (1984). Statistical theory of passive location systems. IEEE Transactions on Aerospace and Electronic Systems, Volume AES-20(Number 2).
- Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions On Information Theory, Volume IT-13(Number 2).
Paper Citation
in Harvard Style
Kräußling A., E. Schneider F. and Wildermuth D. (2004). TRACKING OF EXTENDED CROSSING OBJECTS USING THE VITERBI ALGORITHM . In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 972-8865-12-0, pages 142-149. DOI: 10.5220/0001129601420149
in Bibtex Style
@conference{icinco04,
author={Andreas Kräußling and Frank E. Schneider and Dennis Wildermuth},
title={TRACKING OF EXTENDED CROSSING OBJECTS USING THE VITERBI ALGORITHM},
booktitle={Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2004},
pages={142-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001129601420149},
isbn={972-8865-12-0},
}
in EndNote Style
TY - CONF
JO - Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - TRACKING OF EXTENDED CROSSING OBJECTS USING THE VITERBI ALGORITHM
SN - 972-8865-12-0
AU - Kräußling A.
AU - E. Schneider F.
AU - Wildermuth D.
PY - 2004
SP - 142
EP - 149
DO - 10.5220/0001129601420149