Vehicle Tracking based on Customized Template Matching

Sebastiano Battiato, Giovanni Maria Farinella, Antonino Furnari, Giovanni Puglisi, Anique Snijders, Jelmer Spiekstra

2014

Abstract

In this paper we present a template matching based vehicle tracking algorithm designed for traffic analysis purposes. The proposed approach could be integrated in a system able to understand lane changes, gate passages and other behaviours useful for traffic analysis. After reviewing some state-of-the-art object tracking techniques, the proposed approach is presented as a customization of the template matching algorithm by introducing different modules designed to solve specific issues of the application context. The experiments are performed on a dataset compound by real-world cases of vehicle traffic acquired in different scene contexts (e.g., highway, urban, etc.) and weather conditions (e.g., raining, snowing, etc.). The performances of the proposed approach are compared with respect to a baseline technique based on background-foreground separation.

References

  1. Emilio Maggio and Andrea Cavallaro. Video tracking: theory and practice. Wiley, 2011.
  2. Alper Yilmaz, Omar Javed, and Mubarak Shah. Object tracking: A survey. ACM Computing Surveys, 38(4):13, 2006.
  3. Bruce D. Lucas, Takeo Kanade, et al. An iterative image registration technique with an application to stereo vision. In IJCAI, volume 81, pages 674-679, 1981.
  4. Simon Baker and Iain Matthews. Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision, 56(3):221-255, 2004.
  5. Carlo Tomasi and Takeo Kanade. Detection and tracking of point features. School of Computer Science, Carnegie Mellon Univ., 1991.
  6. Berthold K. P. Horn and Brian G. Schunck. Determining optical flow. Artificial intelligence, 17(1):185-203, 1981.
  7. Sebastiano Battiato, Giovanni Gallo, Giovanni Puglisi, and Salvatore Scellato. Sift features tracking for video stabilization. In Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on, pages 825-830, 2007.
  8. Giovanni M. Farinella and Eugenio Rustico. Low cost finger tracking on flat surfaces. Eurographics Italian Chapter Conference 2008 - Proceedings, pp. 43-48, 2008.
  9. Sebastiano Battiato, Stefano Cafiso, Alessandro Di Graziano, Giovanni M. Farinella, and Oliver Giudice. Road traffic conflict analysis from geo-referenced stereo sequences. International Conference on Image Analysis and Processing, Lecture Notes in Computer Science LNCS 8156, pp. 381-390, 2013.
  10. Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer. Kernel-based object tracking. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(5):564-577, 2003.
  11. Gary R. Bradski. Computer vision face tracking for use in a perceptual user interface. 1998.
  12. Dorin Comaniciu and Peter Meer. Mean shift: A robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(5):603-619, 2002.
  13. Zdenek Kalal, Jiri Matas, and Krystian Mikolajczyk. Online learning of robust object detectors during unstable tracking. In Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, pages 1417-1424. IEEE, 2009.
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Paper Citation


in Harvard Style

Battiato S., Farinella G., Furnari A., Puglisi G., Snijders A. and Spiekstra J. (2014). Vehicle Tracking based on Customized Template Matching . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: PANORAMA, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 755-760. DOI: 10.5220/0004872607550760


in Bibtex Style

@conference{panorama14,
author={Sebastiano Battiato and Giovanni Maria Farinella and Antonino Furnari and Giovanni Puglisi and Anique Snijders and Jelmer Spiekstra},
title={Vehicle Tracking based on Customized Template Matching},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: PANORAMA, (VISIGRAPP 2014)},
year={2014},
pages={755-760},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004872607550760},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: PANORAMA, (VISIGRAPP 2014)
TI - Vehicle Tracking based on Customized Template Matching
SN - 978-989-758-004-8
AU - Battiato S.
AU - Farinella G.
AU - Furnari A.
AU - Puglisi G.
AU - Snijders A.
AU - Spiekstra J.
PY - 2014
SP - 755
EP - 760
DO - 10.5220/0004872607550760