Abstract
People tracking is essential for robots that are supposed to interact with people. The majority of approaches track humans in the vicinity of the robot independently. However, people typically form groups that split and merge. These group formation processes reflect social relations and interactions that we seek to recognize in this paper. To this end, we pose the group tracking problem as a recursive multi-hypothesis model selection problem in which we hypothesize over both, the partitioning of tracks into groups (models) and the association of observations to tracks (assignments). Model hypotheses that include split, merge, and continuation events are first generated in a data-driven manner and then validated by means of the assignment probabilities conditioned on the respective model. Observations are found by clustering points from a laser range finder and associated to existing group tracks using the minimum average Hausdorff distance. We further propose a method to estimate the number of people in the individual groups. Experiments with a mobile robot demonstrate that the approach is able to accurately recover social grouping of people with respect to the ground truth. The results also show that tracking groups is clearly more efficient than tracking people separately. Our system runs in real-time on a typical desktop computer.
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References
Kluge B, Köhler C, Prassler E (2001) Fast and robust tracking of multiple moving objects with a laser range finder. In: Proceedings of the IEEE int. conf. on robotics and automation
Fod A, Howard A, Mataric MJ (2002) Laser-based people tracking. In: IEEE intl. conf. on robotics and automation (ICRA), Washington DC, May 2002, pp 3024–3029
Schulz D, Burgard W, Fox D, Cremers A (2003) People tracking with a mobile robot using sample-based joint probabilistic data association filters. Intl J Robotics Res. 22(2)
Cui J, Zha H, Zhao H, Shibasaki R (2005) Tracking multiple people using laser and vision. In: IEEE/RSJ international conference on intelligent robots and systems, Alberta, Canada
Zajdel W, Zivkovic Z, Kröse B (2005) Keeping track of humans: Have I seen this person before? In: IEEE international conference on robotics and automation, Barcelona, Spain
Taylor G, Kleeman L (2004) A multiple hypothesis walking person tracker with switched dynamic model. In: Proc. of the Australasian conference on robotics and automation, Canberra, Australia
Arras KO, Grzonka S, Luber M, Burgard W (2008) Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities. In IEEE international conference on robotics and automation (ICRA), Pasadena, CA, USA, May 2008
Khan Z, Balch T, Dellaert F (2006) MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements. IEEE Trans. Pattern Analysis Mach Intell 28(12)
Mucientes M, Burgard W (2006) Multiple hypothesis tracking of clusters of people. In: IEEE/RSJ international conference on intelligent robots and systems, October 2006, pp 692–697
McKenna S, Jabri S, Duric Z, Rosenfeld A, Wechsler H (2000) Tracking groups of people. Comput Vis Image Underst 80(1):42–56
Gennari G, Hager GD (2004) Probabilistic data association methods in visual tracking of groups. In: IEEE conference on computer vision pattern recognition (CVPR)
Bose B, Wang X, Grimson E (2007) Multi-class object tracking algorithm that handles fragmentation and grouping. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1–8
Reid DB (1979) An algorithm for tracking multiple targets. IEEE Trans Autom Control AC-24(6):843–854
Cox I, Hingorani S (1996) An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans Pattern Analysis Mach Intell 18(2):138–150
Joo S-W, Chellappa R (2007) A multiple-hypothesis approach for multiobject visual tracking. IEEE Trans Image Process 16(11):2849–2854
Murty K (1968) An algorithm for ranking all the assignments in order of increasing cost. Oper Res 16:682–687
Lau B, Arras KO, Burgard W (2009) Tracking groups of people with a multi-model hypothesis tracker. In: International conference on robotics and automation (ICRA), Kobe, Japan
Hall E (1974) Handbook of proxemics research. Society for the Anthropology of Visual Communications
Arras KO, Martínez Mozos Ó, Burgard W (2007) Using boosted features for the detection of people in 2d range data. In: Proc. IEEE intl. conf. on robotics and automation (ICRA’07), Rome, Italy
Hartigan J (1975) Clustering algorithms. Wiley, New York
Dubuisson MP, Jain AK (1994) A modified Hausdorff distance for object matching. In: Intl. conference on pattern recognition, vol 1, Jerusalem, Israel, pp A:566–568
Cox I, Miller M (1995) On finding ranked assignments with application to multi-target tracking and motion correspondence. IEEE Trans Aerosp Electron Syst 31(1):486–489
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Lau, B., Arras, K.O. & Burgard, W. Multi-model Hypothesis Group Tracking and Group Size Estimation. Int J of Soc Robotics 2, 19–30 (2010). https://doi.org/10.1007/s12369-009-0036-0
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DOI: https://doi.org/10.1007/s12369-009-0036-0