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Tytuł artykułu

Research on human silhouette detection methods for a non-cooperative biometric system

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Języki publikacji
EN
Abstrakty
EN
Together with the development of effective and efficient people identification algorithms, biometric authentication systems become increasingly popular and widespread, leading to a significant growth in the number of institutions interested in implementing and using such systems. Although, several research works focused their efforts on these type of solutions, none of the commonly available systems provide a non-cooperative approach to object identification. For this reason, they are not suitable for use in some specific situations, such as people entering the stadium. Therefore, we decided to go up against these limitations and develop biometric identification system for less constrained scenarios. In this paper, we present an evaluation of different algorithms suitable for human silhouette detection in such environment. We focus on investigating their effectiveness and performance under unconstrained conditions, such as different lighting.
Twórcy
autor
  • Department of Microelectornics and Computer Science, Lodz University of Technology, ul. Wolczanska 221/223, 90-924 Lodz, Poland
  • Department of Microelectornics and Computer Science, Lodz University of Technology, ul. Wolczanska 221/223, 90-924 Lodz, Poland
autor
  • Department of Microelectornics and Computer Science, Lodz University of Technology, ul. Wolczanska 221/223, 90-924 Lodz, Poland
Bibliografia
  • [1] Wheeler, F. W., Perera, A. G. A., Abramovich, G., Bing Yu and Tu, P. H., Stand-off Iris Recognition System, Biometrics: Theory, Applications and Systems, 2nd IEEE International Conference on, 2008
  • [2] Dalal N., Triggs B., Histograms of oriented gradients for human detection, Proceedings of IEEE Converence Computer Vision and Pattern Recognition, 2005
  • [3] W.T. Freeman, M. Roth., Orientation histograms for hand gesture recognition, Intl. Workshop on Automatic Face and Gesture Recognition, IEEE Computer Society, 1995
  • [4] Dalal N., Finding People in Images and Videos, Phd Thesis, Institute National Polytechnique de Grenoble, 2006
  • [5] Viola P., Jones M., Rapid object detection using a boosted cascade of simple features, IEEE Computer Society Conference, pp. 511 - 518, 2001
  • [6] Piccardi M., Background subtraction techniques: a review, In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 3099 - 3104, 2004
  • [7] Stauffer C., Grimson W., Adaptive background mixture models for realtime tracking, CVPR, vol. 02, 1999
  • [8] Zivkovic Z., Improved adaptive Gaussian mixture model for background subtraction, Proceedings of the 17th International Conference on Pattern Recognition, 2004
  • [9] KadewTraKuPong P., Bowden R., An improved adaptive background mixture model for real-time tracking with shadow detection, Video- Based Surveillance Systems, 2002
  • [10] Brehar R., Nedevschi S., A comparative study of pedestrian detection methods using classical Haar and HoG features versus bag of words model computed from Haar and HoG features, Intelligent Computer Communication and Processing (ICCP), 2011
  • [11] Schiele B., Andriluka M., Majer N., Roth S., Wojek C., Visual People Detection âA˘S¸ Different Models, Comparison and Discussion, Proceedings of the IEEE ICRA Workshop on People Detection and Tracking, 2009
  • [12] Grabowski K., Sankowski W., Human tracking in non-cooperative scenarios, Chapter in Springer book, 2014
  • [13] Lienhart R., Maydt J., An Extended Set of Haar-like Features for Rapid Object Detection, IEEE ICIP 2002, Vol. 1, pp. 900-903, 2002
  • [14] Grabowski, K., Napieralski, A., Hardware architecture optimized for iris recognition, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 21 No. 9, pp. 1293-1303, 2011
  • [15] The OpenCV Reference Manual Release 2.4.8.0, 2013
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7aed27fd-b531-4ad8-be54-6f4c85ec6de4
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