Abstract
People typically move and act under the constraints of an environment, making human behavior strongly place-dependent.Motion patterns, the places and the rates at which people appear, disappear, walk or stand are not random but engendered by the environment. In this paper, we learn a non-homogeneous spatial Poisson process to spatially ground human activity events for the purpose of people tracking. We show how this representation can be used to compute refined probability distributions over hypotheses in a multi-hypothesis tracker and to make better, place-dependent predictions of human motion. In experiments with data from a laser range finder, we demonstrate how both extensions lead to more accurate tracking behavior in terms of data association errors and number of track losses. The system runs in real-time on a typical desktop computer.
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References
Kluge, B., Köhler, C., Prassler, E.: Fast and robust tracking of multiple moving objects with a laser range finder. In: Proc. of the Int. Conf. on Robotics & Automation, ICRA (2001)
Fod, A., Howard, A., Mataríc, M.: Laser-based people tracking. In: Proc. of the Int. Conf. on Robotics & Automation, ICRA (2002)
Kleinhagenbrock, M., Lang, S., Fritsch, J., Lömker, F., Fink, G., Sagerer, G.: Person tracking with a mobile robot based on multi-modal anchoring. In: IEEE International Workshop on Robot and Human Interactive Communication (ROMAN), Berlin, Germany (2002)
Schulz, D., Burgard, W., Fox, D., Cremers, A.: People tracking with a mobile robot using sample-based joint probabilistic data association filters. International Journal of Robotics Research (IJRR) 22(2), 99–116 (2003)
Topp, E., Christensen, H.: Tracking for following and passing persons. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Alberta, Canada (2005)
Cui, J., Zha, H., Zhao, H., Shibasaki, R.: Tracking multiple people using laser and vision. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Alberta, Canada (2005)
Mucientes, M., Burgard, W.: Multiple hypothesis tracking of clusters of people. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Beijing, China (2006)
Taylor, G., Kleeman, L.: A multiple hypothesis walking person tracker with switched dynamic model. In: Proc. of the Australasian Conf. on Robotics and Automation, Canberra, Australia (2004)
Cui, J., Zha, H., Zhao, H., Shibasaki, R.: Laser-based interacting people tracking using multi-level observations. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Beijing, China (2006)
Arras, K.O., Grzonka, S., Luber, M., Burgard, W.: Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities. In: Proc. of the Int. Conf. on Robotics & Automation, ICRA (2008)
Reid, D.B.: An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control 24(6) (1979)
Cox, I.J., Hingorani, S.L.: An efficient implementation of reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans. Pattern Anal. Mach. Intell (PAMI) 18(2), 138–150 (1996)
Bar-Shalom, Y., Li, X.-R.: Multitarget-Multisensor Tracking: Principles and Techniques. YBS Publishing, Storrs (1995)
Blackman, S.S.: Multiple hypothesis tracking for multiple target tracking. IEEE Aerospace and Electronic Systems Magazine 19(1), 5–18 (2004)
Bruce, A., Gordon, G.: Better motion prediction for people-tracking. In: Proc. of the Int. Conf. on Robotics & Automation (ICRA), Barcelona, Spain (2004)
Liao, L., Fox, D., Hightower, J., Kautz, H., Schulz, D.: Voronoi tracking: Location estimation using sparse and noisy sensor data. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS (2003)
Mazor, E., Averbuch, A., Bar-Shalom, Y., Dayan, J.: Interacting multiple model methods in target tracking: a survey. IEEE Transactions on Aerospace and Electronic Systems 34(1), 103–123 (1998)
Kwok, C., Fox, D.: Map-based multiple model tracking of a moving object. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 18–33. Springer, Heidelberg (2005)
Arras, K.O., Mozos, Ó.M., Burgard, W.: Using boosted features for the detection of people in 2d range data. In: Proc. of the Int. Conf. on Robotics & Automation (ICRA), Rome, Italy (2007)
Murty, K.: An algorithm for ranking all the assignments in order of increasing cost. Operations Research 16 (1968)
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Luber, M., Tipaldi, G.D., Arras, K.O. (2011). Place-Dependent People Tracking. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19457-3_33
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DOI: https://doi.org/10.1007/978-3-642-19457-3_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19456-6
Online ISBN: 978-3-642-19457-3
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