Real-Time Object Tracking using Gradient Vector Flow

• Autorzy: Azadi S. , Pourjam E. , Nikkerdar A. , Sharifi M.

Warianty tytułu

PL

Wykorzystanie metody GVF w śledzeniu obiektów w czasie rzeczywistym

• Języki publikacji: EN

Abstrakty

EN

In this paper an object tracking system with utilizing optical flow technique, and Gradient Vector Flow (GVF) active contours is presented. Optical flow technique is less sensitive to background structure and does not need to build a model for the background of image so it would need less time to process the image. GVF active snakes have good precision for image segmentation. However, due to the high computational cost, they are not usually applicable. Since precision is one of the important factors in the image segmentation, several methods have been developed to overcome the computational speed. In this paper, we, first, recognize the moving object. Then, the object fame with some pixels surrounding to it, was created. Then, this new frame is sent to the GVF filed calculation procedure. Contour initialization is obtained based on the selected pixels. This approach increases the calculation speed, and therefore makes it possible to use the contour for the tracking. The system was built, and tested with a microcomputer. The results show a speed of 10 to 12 frames per second which is considerably suitable for object tracking approaches.

PL

W artykule przedstawiono system śledzenia obiektu z wykorzystaniem techniki Optic Flow oraz Gradiend Vector Flow. Wykrywanie ruchomego obiektu stanowi pierwszy etap działania, następnie ramka zawierająca obiekt przesyłana jest do algorytmu GVF, gdzie określany jest zarys obiektu. Dzięki temu podejściu możliwe jest wykorzystanie, wymagającego obliczeniowo GVF w śledzeniu obiektów. Przedstawiono wyniki eksperymentalne.

Słowa kluczowe

EN

object tracking; active contour; optical flow

PL

śledzenie obiektów; aktywny kontur; obiekt ruchomy

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 1a
• Strony: 280--283
• Opis fizyczny: Bibliogr. 16 poz., rys.

Twórcy

autor

Azadi S.

• Department Of Electrical And Computer Engineering, Semnan University, Semnan, Iran

autor

Pourjam E.

• Department Of Electrical And Computer Engineering, Semnan University, Semnan, Iran

autor

Nikkerdar A.

• Orthodontic Department , College of Dentistry , Qazvin University of Medical Science , Qazvin , Iran

autor

Sharifi M.

• Department Of Electrical And Computer Engineering, Semnan University, Semnan, Iran

Bibliografia

• [1] H.TANIZAKI, Non-Gaussian state-space modeling of nonstationary time series. J. Amer. Statist. Assoc. 82, 1032–1063, 1987
• [2] Yilmaz, A., Javed, O., and Shah, M., Object tracking: A survey. ACM Comput. Surv. 38, 4, 2006, Article 13, 45 pages
• [3] P. Tissainayagam, and D. Suter, Object tracking in image sequences using point features, Pattern Recognition 38, 2005, 105-113
• [4] D. S. Jang, S. W. Jang, and H. I. Choi, "2D human body tracking with Structural Kalman Filter", Pattern Recognition 35, 2002, 2041 – 2049
• [5] A. Koschan, S. Kang, , J. Paik, B. Abidi, and M. Abidi, Color active shape models for tracking non-rigid objects, Pattern Recognition Letters 24, 2003, 1751–1765
• [6] H. Ning, T. Tan, L. Wang, and W. Hu, "Kinematics-based tracking of human walking in monocular video sequences", Image and Vision Computing 22, 2004, 429–441
• [7] W. Kim, and J. J. Lee, "Object tracking based on the modular active shape model", Mechatronics 15, 2005, 371–402
• [8] I. Razi, and S. Azadi, "Presenting a novel algorithm for moving object detection", 6th ICEENG Conference, Eygpt, 27-29 May, 2008
• [9] D. J. Fleet, and Y. Weiss, "Optical Flow Estimation", Mathematical Models in Computer Vision: The Handbook, Springer, 2005, 239-258
• [10] J. L.Barron, and N. A. Thacker, "Tutorial: Computing 2D and 3D Optical Flow", 2004,Tina Memo No. 2004-012
• [11] LUCAS, B. D. and KANADE, T., An iterative image registration technique with an application to stereo vision. International Joint Conference on Artificial Intelligence, 1981.
• [12] M. Kass, A. Witkin, , D. Terzopoulos, "Snakes: active contour models", Int. J. Comput. Vision 1, 1988, 321–332
• [13] J. Cheng and S. W. Foo, Dynamic Directional Gradient Vector Flow for Snakes, IEEE Transactions on Image Processing, vol. 15, no. 6, June 2006, pp. 1563-1571.
• [14] H. Yu, M. S. Pattichis and M. B. Goens, Robust Segmentation of Freehand Ultrasound Image Slices Using Gradient Vector Flow Fast Geometric Active Contours, IEEE Southwest Symposium on Image Analysis and Interpretation, 2006, pp.115 – 119
• [15] C. Kiser, C. Musial and P. Sen , Accelerating active contour algorithms with the Gradient Diffusion Field, 19th International Conference on Pattern Recognition, 2008 (ICPR 2008), 1 - 4
• [16] N. Paragios, O. Mellina-Gottardoand, V. Ramesh, "Gradient Vector Flow Fast Geodesic Active Contours", 8th IEEE International Conference on Computer Vision (ICCV 2001) Proceedings., 2001, vol.1, 67 – 73