Abstract
A novel hybrid technique for detection and predicting the motion of objects in video stream is presented in this paper. The novelty consists in extension of Savitzky-Golay smoothing filter applying difference approach for tracing object mass center with or without acceleration in noised images. The proposed adaptation of least squares methods for smoothing the fast varying values of motion predicting function permits to avoid the oscillation of that function with the same degree of used polynomial. The better results are obtained when the time of motion interpolation is divided into subintervals, and the function is represented by different polynomials over each subinterval. Therefore, in proposed hybrid technique the spatial clusters with objects in motion are detected by the image difference operator and behavior of those clusters is analyzed using their mass centers in consecutive frames. Then the predicted location of object is computed using modified algorithm of weighted least squares model. That provides the tracing possible routes which now are invariant to oscillation of predicting polynomials and noise presented in images. For irregular motion frequently occurred in dynamic scenes, the compensation and stabilization technique is also proposed in this paper. On base of several simulated kinematics experiments the efficiency of proposed technique is analyzed and evaluated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
G. Farneback. Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field. In ICCV, Vancouver, July 2001, pp. 171–177.
O. Starostenko, A. Ramírez, A. Zehe, G. Burlak. Novel algorithms for estimating motion characteristics within a limited sequence of images, in Book Recent Advances in Interdisciplinary Applied Physics, Elsevier , UK , 2005, pp. 277-281.
K. Bendjilali, F. Berkhuche and T. Jin, Characterizing the exact collision course in the plane for mobile robotics application, in Book Novel Algorithms and Techniques in telecommunications, Automation and Industrial Electronics, Ed. Tarek Sobh, Khaled Elleithy, Springer, 2008 (CISSE 2007 proceedings)
J. T. Tello, O. Starostenko, G.Burlak, “New Motion Prediction Algorithm Invairant to Rotation and Occlusion”, J. Advances in Computer Science in México, Vol. 13. 2005. pp.23-33
B. Jahne, Digital image processing, 5ed., Springer, 2002
G. Papadopoulos, R. Bryant, W. Pitts, “Flow Characterization of Flickering Methane/Air Diffusion Flames Using Particle Image Velocimetry”, J. Experiments in Fluids, Vol. 33, No. 3, 2002, pp. 472-481.
W. Gander, J.Hrebícek U von Matt. Smoothing Filters. Solving Problems in Scientific Computing Using Maple and MATLAB, Springer, Paperback, Jul 27, 2004.
J. Wolberg, Data Analysis Using the Method of Linear Squares, Kindle Ed., 2006.
C. Radhakrishna Rao, H.Toutenburg, Heumann, Linear Models and Generalization: Least Squares and Alternatives, Springer, 2007.
R. Chan, C.Greif, Milestones in Matrix Computation: The selected works of Gene H. Golub, Oxford Science publications, 2007.
S. C. Di Pittinuri, Human & Machine Perception: Communication, Interaction, and Integration, NY, World Scientific Publishing Company, 2005.
J Ramsay, B W Silverman, Functional Data Analysis, Springer, USA, 2005.
A. Grebennikov, Método de Splines: Elementos teóricos, Algorítmos y Programas, Max Press, Moscow, 2008.
Acknowledgment
This research is sponsored by Mexican National Council of Science and Technology, CONACyT, Projects: #48259, #109115 and #109417.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this paper
Cite this paper
Starostenko, O., Tello-Martínez, J., Alarcon-Aquino, V., Rodriguez-Asomoza, J., Rosas-Romero, R. (2010). An Extension of Least Squares Methods for Smoothing Oscillation of Motion Predicting Function. In: Sobh, T., Elleithy, K. (eds) Innovations in Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9112-3_48
Download citation
DOI: https://doi.org/10.1007/978-90-481-9112-3_48
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-9111-6
Online ISBN: 978-90-481-9112-3
eBook Packages: Computer ScienceComputer Science (R0)