Towards a Tracking Algorithm based on the Clustering of Spatio-temporal Clouds of Points

Andrea Cavagna, Chiara Creato, Lorenzo Del Castello, Stefania Melillo, Leonardo Parisi, Massimiliano Viale

Abstract

The interest in 3D dynamical tracking is growing in fields such as robotics, biology and fluid dynamics. Recently, a major source of progress in 3D tracking has been the study of collective behaviour in biological systems, where the trajectories of individual animals moving within large and dense groups need to be reconstructed to understand the behavioural interaction rules. Experimental data in this field are generally noisy and at low spatial resolution, so that individuals appear as small featureless objects and trajectories must be retrieved by making use of epipolar information only. Moreover, optical occlusions often occur: in a multicamera system one or more objects become indistinguishable in one view, potentially subjected to loss of identity over long-time trajectories. The most advanced 3D tracking algorithms overcome optical occlusions making use of set-cover techniques, which however have to solve NP-hard optimization problems. Moreover, current methods are not able to cope with occlusions arising from actual physical proximity of objects in 3D space. Here, we present a new method designed to work directly on (3D + 1) clouds of points representing the full spatio-temporal evolution of the moving targets. We can then use a simple connected components labeling routine, which is linear in time, to solve optical occlusions, hence lowering from NP to P the complexity of the problem. Finally, we use normalized cut spectral clustering to tackle 3D physical proximity.

References

  1. Butail, S., Paley, D., et al. (2010). 3d reconstruction of fish schooling kinematics from underwater video. In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages 2438-2443. IEEE.
  2. Cheng, X. E., Qian, Z.-M., Wang, S. H., Jiang, N., Guo, A., and Chen, Y. Q. (2015). A novel method for tracking individuals of fruit fly swarms flying in a laboratory flight arena. PloS one, 10(6):e0129657.
  3. Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C. (2009). Introduction to Algorithms, Third Edition. The MIT Press, 3rd edition.
  4. Dell, A. I., Bender, J. A., Branson, K., Couzin, I. D., de Polavieja, G. G., Noldus, L. P., Pérez-Escudero, A., Perona, P., Straw, A. D., Wikelski, M., et al. (2014). Automated image-based tracking and its application in ecology. Trends in ecology & evolution, 29(7):417- 428.
  5. Ess, A., Schindler, K., Leibe, B., and Van Gool, L. (2010). Object detection and tracking for autonomous navigation in dynamic environments. The International Journal of Robotics Research, 29(14):1707-1725.
  6. Giardina, I. (2008). Collective behavior in animal groups: theoretical models and empirical studies. HFSP journal, 2(4):205-219.
  7. Hampapur, A., Brown, L., Connell, J., Ekin, A., Haas, N., Lu, M., and Pankanti, S. (2005). Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking. Signal Processing Magazine, IEEE, 22(2):38-51.
  8. Hartley, R. I. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, second edition.
  9. Michel, P., Chestnutt, J., Kagami, S., Nishiwaki, K., Kuffner, J., and Kanade, T. (2007). Gpu-accelerated real-time 3d tracking for humanoid locomotion and stair climbing. In Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, pages 463-469. IEEE.
  10. Moussaid, M., Guillot, E. G., Moreau, M., Fehrenbach, J., Chabiron, O., Lemercier, S., Pettré, J., Appert-Rolland, C., Degond, P., and Theraulaz, G. (2012). Traffic instabilities in self-organized pedestrian crowds. PLoS Comput. Biol, 8(3):e1002442.
  11. Ouellette, N. T., Xu, H., and Bodenschatz, E. (2006). A quantitative study of three-dimensional lagrangian particle tracking algorithms. Experiments in Fluids, 40(2):301-313.
  12. Pérez-Escudero, A., Vicente-Page, J., Hinz, R. C., Arganda, S., and de Polavieja, G. G. (2014). idtracker: tracking individuals in a group by automatic identification of unmarked animals. Nature methods, 11(7):743-748.
  13. Puckett, J. G., Kelley, D. H., and Ouellette, N. T. (2014). Searching for effective forces in laboratory insect swarms. Scientific reports , 4.
  14. Shi, J. and Malik, J. (2000). Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8):888-905.
  15. Sobral, A. and Bouwmans, T. (2014). Bgs library: A library framework for algorithms evaluation in foreground/background segmentation. In Background Modeling and Foreground Detection for Video Surveillance. CRC Press, Taylor and Francis Group.
  16. Stockman, G. and Shapiro, L. G. (2001). Computer Vision. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1st edition.
  17. Straw, A. D., Branson, K., Neumann, T. R., and Dickinson, M. H. (2010). Multi-camera real-time threedimensional tracking of multiple flying animals. Journal of The Royal Society Interface, page rsif20100230.
  18. Vacchetti, L., Lepetit, V., and Fua, P. (2004). Combining edge and texture information for real-time accurate 3d camera tracking. In Mixed and Augmented Reality, 2004. ISMAR 2004. Third IEEE and ACM International Symposium on, pages 48-56. IEEE.
  19. Wu, Z., Hristov, N. I., Kunz, T. H., and Betke, M. (2009). Tracking-reconstruction or reconstructiontracking? comparison of two multiple hypothesis tracking approaches to interpret 3d object motion from several camera views. In Motion and Video Computing, 2009. WMVC'09. IEEE Workshop on, pages 1-8. IEEE.
  20. Wu, Z., Kunz, T. H., and Betke, M. (2011). Efficient track linking methods for track graphs using network-flow and set-cover techniques. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1185-1192. IEEE.
Download


Paper Citation


in Harvard Style

Cavagna A., Creato C., Del Castello L., Melillo S., Parisi L. and Viale M. (2016). Towards a Tracking Algorithm based on the Clustering of Spatio-temporal Clouds of Points . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 679-685. DOI: 10.5220/0005770106790685


in Bibtex Style

@conference{visapp16,
author={Andrea Cavagna and Chiara Creato and Lorenzo Del Castello and Stefania Melillo and Leonardo Parisi and Massimiliano Viale},
title={Towards a Tracking Algorithm based on the Clustering of Spatio-temporal Clouds of Points},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={679-685},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005770106790685},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Towards a Tracking Algorithm based on the Clustering of Spatio-temporal Clouds of Points
SN - 978-989-758-175-5
AU - Cavagna A.
AU - Creato C.
AU - Del Castello L.
AU - Melillo S.
AU - Parisi L.
AU - Viale M.
PY - 2016
SP - 679
EP - 685
DO - 10.5220/0005770106790685