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Comparison of tracking methods in respect of automation of an animal behavioral test

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Automation in experiments carried out on animals is getting more and more important in research. Computers take over laborious and time-consuming activities like recording and analysing images of the experiment scene. The first step in an image analysis is finding and distinguishing between the observed animals and then tracking all objects during the experiment. In this paper four tracking methods are presented. Quantitative and qualitative figures of merit are applied to confront those methods. The comparison takes into consideration the level of correct object recognition during different disturbances, the speed of computation, requirements as to the frame rate and image illumination, quality of recovering from occluded situations and others.
Rocznik
Strony
91--104
Opis fizyczny
Bibliogr. 17 poz., rys., tab., wykr.
Twórcy
  • Gdańsk University of Technology, Faculty of Electronics, Telecommunication and Informatics, Department of Biomedical Engineering, Narutowicza 11/12, 80-952 Gdańsk, Poland, magda@biomed.eti.pg.gda.pl
Bibliografia
  • [1] Yilmaz, A., Javed, O., Shah, M. (2006). Object Tracking: A Survey. ACM Computing SurveysI, 38 (4).
  • [2] Albonetti, M.E., Farabollini, F. (1994). Social stress by repeated defeat: effects on social behaviour and emotionality. Behav Brain Res, 62, 187-93.
  • [3] Comaniciu, D., Meer, P. (1999). Mean Shift Analysis and Applications. IEEE Int’l Conf. Comp. Vis., 2, 1197-1203.
  • [4] Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition. Boston: Academic Press.
  • [5] Bradski, G., Kaehler, A. (2008). Learning OpenCV. O’Reilly Media. Inc.
  • [6] Bradski, G.R. (1998). Computer Vision Face Tracking For Use in a Perceptual User Interface. Intel Technology Journal Q2.
  • [7] Horn, B.K.P., Schunck, B.G. (1981). Determining Optical Flow. Artificial Intelligence, 17, 185-203.
  • [8] Lucas, B.D., Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. Proceedings of the 1981 DARPA Imaging Understanding Workshop, 121-130.
  • [9] Isard, M., Blake, A. (1998). CONDENSATION - Conditional Density Propagation for Visual Tracking. Int. J. Computer Vision, 29 (1), 5-28.
  • [10] Arulampalam, M.S., Maskel, S., Gordon, N., Clapp, T. (2002). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 50 (2), 174-187.
  • [11] Bhandarkar, S.M., Luo, X. (2009). Integrated detection and tracking of multiple faces using particle filtering and optical flow-based elastic matching. Computer Vision and Image Understanding, 113, 708-725.
  • [12] Thuy, M., Leon, F.P. (2009). Non-linear multimodal object tracking based on 2D lidar data. Metrol. Meas. Syst., 16 (3), 359-369.
  • [13] Gordon, N., Salmond, D., Smith, A.F.M. (1993). Novel approach to non-linear and non-Gaussian Bayesian state estimation. Proc. Inst. Elect. Eng., 140, 107-113.
  • [14] Pitt, M., Shephard, N. (1999). Auxiliary particle filters. J. Amer. Statist. Assoc., 94, 590-599.
  • [15] Musso, C., Oudjane, N., LeGland, F. (2001). Improving regularized particle filters. Sequential Monte Carlo Methods in Practise, New York: Springer-Verlag.
  • [16] Shan, C., Wei, Y., Tan, T., Ojardias, F. (2004). Real time hand tracking by combining particle filtering and mean shift. Proc. of IEEE International Conference on Automatic Face and Gesture Recognition, 669-674.
  • [17] Kass, M., Witkin, A., Terzopoulos, D. (1988). Snakes: Active Contour Models. International Journal of Computer Vision, 321-331
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0075-0020
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