Abstract
We have recently developed an original tracking framework allowing to track textured templates in real time [3]. This framework is based on the use of difference images, the difference between the target template and the template included in predicted area of interest. For efficiency purposes, the difference image was limited to a few points belonging to the area of interest. The measurements were therefore very punctual. In this article a wavelet representation of the area of interest is used as a substitute for this punctual measurement. In this case, the difference image is a difference of wavelet parameters (difference between the wavelet representation of the target and the wavelet representation of the template included in the current position of the region of interest). This algorithm is a part of a real-time system aiming at automatically detecting and tracking vehicles in video sequences.
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References
C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In IEEE Conference on Computer Vision, pp 555–562, 1998.
F. Jurie and M. Dhome. Real time 3d template matching. In Computer Vision and Pattern Recongition, pages (I) 791–797, Hawai, December 2001.
F. Jurie and M. Dhome. Real time template matching. In Proc. IEEE International Conference on Computer vision, pages 544–549, Vancouver, Canada, July 2001.
N.D. Matthews, P.E. An, D. Charnley, and C.J. Harris. Vehicle detection and recognition in greyscale imagery. In 2nd Int. Workshop on Intelligent Autonomous Vehicles, pages 1–6, Helsinki, 1995. IFAC.
P. Viola and M. Jones. Robust Real-time Object Detection. In Second International Workshop on statistical and computational theories of vision-modeling, learning, computing, and sampling, Vancouver, Canada, 13 July 2001.
R. Aufrere, R. Chapuis, F. Chausse, and J. Alizon. A fast and robust visionbased road following algorithm. In IV’2001 (IEEE Int. Conf. on Intelligent Vehicles, pages 13–18, May 2001. Tokio, Japan.
P. Sayd, R. Chapuis, R. Aufrere, and F. Chausse. A dynamic vision algorithm to recover the 3d shape of a non-structured road. In Proceedings of the IEEE International Conference on Intelligent Vehicles, pp 80–86, Stuttgart, Germany, October 1998.
C. Tzomakas and W. von Seelen. Vehicle detection in traffic scenes using shadows. Technical report, IRINI 98-06, Institut fur Neuroinformatik, Ruhr-Universitat Bochum, D-44780 Bochum, Germany, August 1998.
Marinus B. van Leeuwen and Frans C.A. Groen. Vehicle detection with a mobile camera. Technical report, Computer Science Institute, University of Amsterdam, The Netherlands, October 2001.
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Chateau, T., Jurie, F., Dhome, M., Clady, X. (2002). Real-Time Tracking Using Wavelet Representation. In: Van Gool, L. (eds) Pattern Recognition. DAGM 2002. Lecture Notes in Computer Science, vol 2449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45783-6_63
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DOI: https://doi.org/10.1007/3-540-45783-6_63
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