Warianty tytułu
Języki publikacji
Abstrakty
The aim of the article is to review and implement methods of tracking objects in sequences of video image. The outcome of the test is to indicate the method that fulfils assumptions concerning the amount of correctly recognisable image pixels and the work speed of chosen algorithm. The following methods of tracking objects were tested: optical flow, CAMshift method (Continuously Adaptive Mean Shift), Mixture of Gaussians and SURF method (Speeded-Up Robust Features).
Twórcy
autor
- Maritime University of Szczecin
Bibliografia
- 1. R. Tadeusiewicz and P. Kordocha, Komputerowa analiza i przetwarzanie obrazów. Kraków: Wydawnictwo Fundacji Postępy Telekomunikacji,1997.
- 2. N. Nourani-vatani, P. V. K. Borges, and J. M. Roberts, “A Study of Feature Extraction Algorithms for Optical Flow Tracking,” 2006.
- 3. A. de La Bourdonnaye, R. Doskočil, V. Křivánek, and A. Štefek, “Practical Experience with Distance Measurement Based on Single Visual Camera,” Adv. Mil. Technol., vol. 7, no. 2, 2012.
- 4. J. Bouguet, “Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm,” vol. 1, no. 2, pp. 1–9.
- 5. K. Pałczyński, “Segmentacja na podstawie analizy pola ruchu sekwencji obrazów cyfrowych,” Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie, 2002.
- 6. K. Fukunaga, Introduction to Statistical Pattern Stas-tical Pattern Recognition, Second Edi. Indiana: Academic Press, 1990.
- 7. G. R. Bradski, “Computer Vision Face Tracking For Use in a Perceptual User Interface,” Intel Technol. J., 1998.
- 8. T. Bouwmans, F. El Baf, and B. Vachon, “Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey,”Recent Patents Comput. Sci., vol. 1, no. 3, pp. 219–237, Nov. 2008.
- 9. C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” Proceedings. 1999 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (Cat. No PR00149), pp. 246–252, 1999.
- 10. H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robust features,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, vol. 3951 LNCS, pp. 404–417.
- 11. J. J. Anitha and S. M. Deepa, “Tracking and Recognition of Objects using SURF Descriptor and Harris Corner Detection,” Int. J. Curr. Eng. Technol., vol. 4, no. 2, pp. 775–778, 2014.
- 12. H. Kandil and A. Atwan, “A Comparative Study between SIFT- Particle and SURF-Particle Video Tracking Algorithms,” Int. J. Signal Process. Image Process. Pattern Recognit., vol. 5, no. 3, pp. 111–122, 2012.
- 13. M. Bzdawski, “Śledzenie obiektów w sekwencjach obrazów,” Politechnika Warszawska, 2008.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-6204f936-179f-4aad-82ff-1c483a40f46f