Abstract
This paper proposes a fast and robust multi-people tracking algorithm for mobile platforms equipped with a RGB-D sensor. Our approach features an efficient point cloud depth-based clustering, an HOG-like classification to robustly initialize a person tracking and a person classifier with online learning to manage the person ID matching even after a full occlusion. For people detection, we make the assumption that people move on a ground plane. Tests are presented on a challenging real-world indoor environment and results have been evaluated with the CLEAR MOT metrics. Our algorithm proved to correctly track 96% of people with very limited ID switches and few false positives, with an average frame rate of 25 fps. Moreover, its applicability to robot-people following tasks have been tested and discussed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bajracharya, M., Moghaddam, B., Howard, A., Brennan, S., Matthies, L.H.: A fast stereo-based system for detecting and tracking pedestrians from a moving vehicle. International Journal of Robotics Research 28, 1466–1485 (2009)
Bellotto, N., Hu, H.: Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of bayesian filters. Auton. Robots 28, 425–438 (2010)
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. J. Image Video Process. 2008, 1:1–1:10 (2008)
Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: IEEE International Conference on Computer Vision (October 2009)
Carballo, A., Ohya, A., Yuta, S.: People detection using range and intensity data from multi-layered laser range finders. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5849–5854 (2010)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (June 2005)
Ess, A., Leibe, B., Schindler, K., Van Gool, L.: A mobile vision system for robust multi-person tracking. In: IEEE Conference on Computer Vision and Pattern Recognition 2008, pp. 1–8 (2008)
Ess, A., Leibe, B., Schindler, K., Van Gool, L.: Moving obstacle detection in highly dynamic scenes. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Piscataway, NJ, USA, pp. 4451–4458 (2009)
Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88, 303–338 (2010)
Grabner, H., Bischof, H.: On-line boosting and vision. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1, Washington, DC, USA, pp. 260–267 (2006)
Konstantinova, P., Udvarev, A., Semerdjiev, T.: A study of a target tracking algorithm using global nearest neighbor approach. In: Proceedings of the 4th International Conference Conference on Computer Systems and Technologies: e-Learning, New York, NY, USA, pp. 290–295 (2003)
Luber, M., Spinello, L., Arras, K.O.: People tracking in rgb-d data with on-line boosted target models. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011 (2011)
Martin, C., Schaffernicht, E., Scheidig, A., Gross, H.-M.: Multi-modal sensor fusion using a probabilistic aggregation scheme for people detection and tracking. Robotics and Autonomous Systems 54(9), 721–728 (2006)
Mozos, O., Kurazume, R., Hasegawa, T.: Multi-part people detection using 2d range data. International Journal of Social Robotics 2, 31–40 (2010)
Navarro-Serment, L.E., Mertz, C., Hebert, M.: Pedestrian detection and tracking using three-dimensional ladar data. In: FSR, pp. 103–112 (2009)
Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13 (2011)
Satake, J., Miura, J.: Robust stereo-based person detection and tracking for a person following robot. In: Workshop on People Detection and Tracking IEEE ICRA (2009)
Spinello, L., Arras, K.O.: People detection in rgb-d data. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011 (2011)
Spinello, L., Arras, K.O., Triebel, R., Siegwart, R.: A layered approach to people detection in 3d range data. In: Proc. 24th AAAI Conference on Artificial Intelligence, PGAI Track (AAAI 2010), Atlanta, USA (2010)
Spinello, L., Luber, M., Arras, K.O.: Tracking people in 3d using a bottom-up top-down people detector. In: IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai (2011)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR 2001, vol. 1, pp. 511–518 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Basso, F., Munaro, M., Michieletto, S., Pagello, E., Menegatti, E. (2013). Fast and Robust Multi-people Tracking from RGB-D Data for a Mobile Robot. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33926-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-33926-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33925-7
Online ISBN: 978-3-642-33926-4
eBook Packages: EngineeringEngineering (R0)