MTTV - An Interactive Trajectory Visualization and Analysis Tool

Fabio Poiesi, Andrea Cavallaro

Abstract

We present an interactive visualizer that enables the exploration, measurement, analysis and manipulation of trajectories. Trajectories can be generated either automatically by multi-target tracking algorithms or manually by human annotators. The visualizer helps understanding the behavior of targets, correcting tracking results and quantifying the performance of tracking algorithms. The input video can be overlaid to compare ideal and estimated target locations. The code of the visualizer (C++ with openFrameworks) is open source.

References

  1. Bazzani, L., Zanotto, M., Cristani, M., and Murino, V. (2014). Joint individual-group modeling for tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2014.2353641.
  2. Beheiry, M. E. and Dahan, M. (2013). ViSP: representing single-particle localizations in three dimensions. Nature Methods, 10(8):689-690.
  3. Buschmann, S., Trapp, M., Luhne, P., and Dollner, J. (2014). Hardware-accelerated attribute mapping for interactive visualization of complex 3D trajectories. In Proc. of International Conference on Information Visualization Theory and Applications, pages 355-363, Lisbon, Portugal.
  4. Couvillon, M., Phillipps, H., Schurch, R., and Ratnieks, F. (2012). Working against gravity: horizontal honeybee waggle runs have greater angular scatter than vertical waggle runs. Biology Letters, 8(4):1-4.
  5. Fasciano, T., Dornhaus, A., and Shin, M. (2014). Ant tracking with occlusion tunnels. In Proc. of Winter Conference on Applications of Computer Vision, pages 947- 952, Steamboat Springs, CO, USA.
  6. Helbing, D., Farkas, I., and Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature, 407(9):487-490.
  7. Hoferlin, M., Hoferlin, B., and Weiskopf, D. (2009). Video visual analytics of tracked moving objects. In Proc. of Workshop on Behaviour Monitoring and Interpretation, pages 59-64, Ghent, BEL.
  8. Hurter, C., Tissoires, B., and Conversy, S. (2009). FromDaDy: Spreading aircraft trajectories across views to support iterative queries. IEEE Trans. on Visualization and Computer Graphics, 15(6):1017-1024.
  9. Joshi, A. and Rheingans, P. (2005). Illustration-inspired techniques for visualizing time-varying data. In Proc. of Visualization, pages 679-686, Minneapolis, MN, USA.
  10. Khan, Z., Balch, T., and Dellaert, F. (2005). MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(11):1805-1819.
  11. Kimura, T., Ohashi, M., Crailsheim, K., Schmickl, T., Okada, R., Radspieler, G., and Ikeno, H. (2014). Development of a new method to track multiple honey bees with complex behaviors on a flat laboratory arena. Plos One, 9(1):1-12.
  12. Li, K. et al. (2008). Cell population tracking and lineage construction with spatiotemporal context. Medical Image Analysis, 12(5):546-566.
  13. Li, Y., Huang, C., and Nevatia, R. (2009). Learning to associate: hybridboosted multi-target tracker for crowded scene. In Proc. of Computer Vision and Pattern Recognition, pages 2953-2960, Miami, FL, USA.
  14. Milan, A., Gade, R., Dick, A., Moeslund, T., and Reid, I. (2014). Improving global multi-target tracking with local updates. In Proc. of European Conference on Computer Vision Workshops, Zurich, CH.
  15. Nawaz, T., Cavallaro, A., and Rinner, B. (2014a). Trajectory clustering for motion pattern extraction in aerial videos. In Proc. of International Conference on Image Processing, Paris, FR.
  16. Nawaz, T., Poiesi, F., and Cavallaro, A. (2014b). Measures of effective video tracking. Trans. on Image Processing, 23(1):376-388.
  17. Park, C., Woehl, T., Evans, J., and Browning, N. (2014). Minimum cost multi-way data association for optimizing multitarget tracking of interacting objects. IEEE Trans. on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2014.2346202.
  18. Poiesi, F. and Cavallaro, A. (2014). Tracking multiple high-density homogeneous targets. IEEE Trans. on Circuits and Systems for Video Technology, DOI: 10.1109/TCSVT.2014.2344509.
  19. Pylyshyn, Z. (2003). Seeing and Visualizing: It's not what you think (Life and Mind). Bradford Book.
  20. Ross, D., Lim, J., Lin, R.-S., and Yang, M.-H. (2008). Incremental learning for robust visual tracking. International Journal on Computer Vision, 77(1-3):125-141.
  21. SanMiguel, J., Cavallaro, A., and Martinez, J. (2012). Adaptive on-line performance evaluation of video trackers. IEEE Trans. on Image Processing, 21(5):2812-2823.
  22. Shitrit, H. et al. (2014). Multi-commodity network flow for tracking multiple people. IEEE Trans. on Pattern Analysis and Machine Intelligence, 36(8):1614-1627.
  23. Sochman, J. and Hogg, D. (2011). Who knows who - inverting the Social Force Model for finding groups. In Proc. of Internation Conference on Computer Vision Workshops, pages 830-837, Barcelona, Spain.
  24. Solera, F., Calderara, S., and Cucchiara, R. (2013). Structured learning for detection of social groups in crowd. In Proc. of Advanced Video and Signal-Based Surveillance, pages 7-12, Krakow, Poland.
  25. Tominski, C., Schumann, H., Andrienko, G., and Andrienko, N. (2012). Stacking-based visualization of trajectory attribute data. IEEE Trans. on Visualization and Computer Graphics, 18(12):2565-2574.
  26. Veeraraghavan, A., Chellappa, R., and Srinivasan, M. (2008). Shape-and-behavior encoded tracking of bee dances. IEEE Trans. on Pattern Analysis and Machine Intelligence, 30(3):463-476.
  27. Whitehorn, L., Hawkes, F., and Dublon, I. (2013). Superplot3D: an open source GUI tool for 3D trajectory visualisation and elementary processing. Source code for biology and medicine, 8(19):1-4.
  28. Wong, B. (2012). Points of view: visualization biological data. Nature Methods, 9(12):1131.
  29. Wu, B. and Nevatia, R. (2006). Tracking of multiple, partially occluded humans based static body part detection. In Proc. of Computer Vision and Pattern Recognition, pages 951-958, New York, USA.
  30. Yang, B. and Nevatia, R. (2012). An online learned CRF model for multi-target tracking. In Proc. of Computer Vision and Pattern Recognition, pages 2034- 2041, Providence, RI.
  31. Yang, B. and Nevatia, R. (2014). Multi-target tracking by online learning a CRF model of appearance and motion patterns. International Journal on Computer Vision, 107(2):203-217.
  32. Yin, F., Makris, D., and Velastin, S. (2007). Performance evaluation of object tracking algorithms. In WPETS, Rio de Janeiro, Brazil.
  33. Zhang, S., Wang, J., Wang, Z., Gong, Y., and Liu, Y. (2015). Multi-target tracking by learning local-to-global trajectory models. Pattern Recognition, 48(2):580-590.
  34. Zhang, T., Hanqing, L., and Li, S. (2009). Learning semantic scene models by object classification and trajectory clustering. In Proc. of Computer Vision and Pattern Recognition, pages 1940-1947, Miami, FL, USA.
Download


Paper Citation


in Harvard Style

Poiesi F. and Cavallaro A. (2015). MTTV - An Interactive Trajectory Visualization and Analysis Tool . In Proceedings of the 6th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2015) ISBN 978-989-758-088-8, pages 157-162. DOI: 10.5220/0005311001570162


in Bibtex Style

@conference{ivapp15,
author={Fabio Poiesi and Andrea Cavallaro},
title={MTTV - An Interactive Trajectory Visualization and Analysis Tool},
booktitle={Proceedings of the 6th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2015)},
year={2015},
pages={157-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005311001570162},
isbn={978-989-758-088-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2015)
TI - MTTV - An Interactive Trajectory Visualization and Analysis Tool
SN - 978-989-758-088-8
AU - Poiesi F.
AU - Cavallaro A.
PY - 2015
SP - 157
EP - 162
DO - 10.5220/0005311001570162