Multiple Camera Human Detection and Tracking Inside a Robotic Cell - An Approach based on Image Warping, Computer Vision, K-d Trees and Particle Filtering

Matteo Ragaglia, Luca Bascetta, Paolo Rocco

2014

Abstract

In an industiral scenario the capability to detect and track human workers entering a robotic cell represents a fundamental requirement to enable safe and efficient human-robot cooperation. This paper proposes a new approach to the problem of Human Detection and Tracking based on low-cost commercial RGB surveillance cameras, image warping techniques, computer vision algorithms, efficient data structures such as kdimensional trees and particle filtering. Results of several validation experiments are presented.

References

  1. Arechavaleta, G., Laumond, J.-P., Hicheur, H., and Berthoz, A. (2008). An optimality principle governing human walking. Robotics, IEEE Transactions on, 24(1):5- 14.
  2. Asaula, R., Fontanelli, D., and Palopoli, L. (2010). Safety provisions for human/robot interactions using stochastic discrete abstractions. In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pages 2175-2180.
  3. Bascetta, L., Ferretti, G., Rocco, P., Ardo, H., Bruyninckx, H., Demeester, E., and Di Lello, E. (2011). Towards safe human-robot interaction in robotic cells: An approach based on visual tracking and intention estimation. In Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pages 2971- 2978.
  4. Bradski, G. (2000). The OpenCV Library. Dr. Dobb's Journal of Software Tools.
  5. Bradski, G. and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV library. O'Reilly Media.
  6. Elshafie, M. and Bone, G. (2008). Markerless human tracking for industrial environments. In Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on, pages 001139-001144.
  7. Hartley, R. I. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, second edition.
  8. KaewTraKulPong, P. and Bowden, R. (2002). An improved adaptive background mixture model for realtime tracking with shadow detection. In Remagnino, P., Jones, G., Paragios, N., and Regazzoni, C., editors, Video-Based Surveillance Systems, pages 135- 144. Springer US.
  9. Kulic, D. and Croft, E. (2007). Pre-collision safety strategies for human-robot interaction. Auton. Robots, 22(2):149-164.
  10. Moore, A. (1991). A tutorial on kd-trees. Extract from PhD Thesis. Available from http: //www.ri.cmu.edu/pub_files/pub1/moore_ andrew_1991_1/moore_andrew_1991_1.pdf.
  11. Mosberger, R. and Andreasson, H. (2013). An inexpensive monocular vision system for tracking humans in industrial environments. In Robotics and Automation (ICRA), 2013 IEEE International Conference on, pages 5850-5857.
  12. Mosberger, R., Andreasson, H., and Lilienthal, A. (2013). Multi-human tracking using high-visibility clothing for industrial safety. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, pages 638-644.
  13. Munaro, M., Basso, F., and Menegatti, E. (2012). Tracking people within groups with rgb-d data. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages 2101-2107.
  14. Munaro, M., Lewis, C., Chambers, D., Hvass, P., and Menegatti, E. (2013). Rgb-d human detection and tracking for industrial environments. In 13th International Conference on Intelligent Autonomous Systems (IAS-13). accepted.
  15. Najmaei, N., Kermani, M., and Al-Lawati, M. (2011). A new sensory system for modeling and tracking humans within industrial work cells. Instrumentation and Measurement, IEEE Transactions on, 60(4):1227-1236.
  16. Rogez, G., Orrite, C., Guerrero, J., and Torr, P. H. (2014). Exploiting projective geometry for viewinvariant monocular human motion analysis in manmade environments. Computer Vision and Image Understanding, 120(0):126 - 140.
  17. Zivkovic, Z. (2004). Improved adaptive gaussian mixture model for background subtraction. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, volume 2, pages 28-31 Vol.2.
  18. Zivkovic, Z. and van der Heijden, F. (2006). Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett., 27(7):773-780.
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Paper Citation


in Harvard Style

Ragaglia M., Bascetta L. and Rocco P. (2014). Multiple Camera Human Detection and Tracking Inside a Robotic Cell - An Approach based on Image Warping, Computer Vision, K-d Trees and Particle Filtering . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-040-6, pages 374-381. DOI: 10.5220/0005045703740381


in Bibtex Style

@conference{icinco14,
author={Matteo Ragaglia and Luca Bascetta and Paolo Rocco},
title={Multiple Camera Human Detection and Tracking Inside a Robotic Cell - An Approach based on Image Warping, Computer Vision, K-d Trees and Particle Filtering},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2014},
pages={374-381},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005045703740381},
isbn={978-989-758-040-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Multiple Camera Human Detection and Tracking Inside a Robotic Cell - An Approach based on Image Warping, Computer Vision, K-d Trees and Particle Filtering
SN - 978-989-758-040-6
AU - Ragaglia M.
AU - Bascetta L.
AU - Rocco P.
PY - 2014
SP - 374
EP - 381
DO - 10.5220/0005045703740381