Safe City

Safe City

Real-time video tracking of objects using modified Kernel Base Object Tracking algorithm with more accurate detection capability in security and surveillance products

Document Type : Original Article

Author
Majid Shakeri
Electrical Department, Shahriar Branch, Islamic Azad University, Shahriar, Tehran, Iran (Corresponding Author)
10.22034/ispdrc.2021.696745
Abstract
One of the fields of visual observation is the moving object tracking machine .Detection of a
moving object is a real-time process in which the position of an animated object is determined at
any time .For this purpose, in this article, we tried to work with a practical approach and result in
the production of the product and using the OpenCV library in the Visual C ++ programming
environment to try to create an object in successive frames using a Pan Tilt Zoom camera, a
camera with a platform Two degrees of freedom are tracked promptly .Also, in this paper, we
investigate a histogram based on the Kernel Base Object Tracking algorithm and its performance
is compared with the Normalized Cross Correlation algorithm based on the statistical correlation
coefficient .The purpose of this article is to translate mathematical formulas and materials into
codes that are easy to use in surveillance and security.
Keywords
Machine Vision
Real-time video tracking
Kernel Base Object Tracking Algorithm
[1] Klette, R. (2014). An Introduction into Theory and Algorithms, Springer-Verlag Concise Computer
Vision.
[2]Comaniciu, D. & et al, (2003). Kernel-based Object Tracking, IEEE Trans. Pattern Anal. Machine Intell.
[3] Elgammal, A. & et al, (2003). Efficient Kernel Density Estimation Using the Fast Gauss Transform with
Applications to Color Modeling and Tracking, IEEE Transactions on Pattern Analysis and Machine
Intelligence.
[4]Chang, C. & Ansari, R. (2005). Kernel Particle Filter for Visual Tracking, IEEE Signal Processing Letters.
[5] Yilmaz, A. (2007). Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation
Selection, IEEE Conference on Computer Vision and Pattern Recognition.
[6]Caseiro, R. & et al, (2015). High-Speed Tracking with Kernelized Correlation Filters, IEEE Transactions
on Pattern Analysis and Machine Intelligence.
[7]Venkatesh Babua, R. & Pérez, P. (2007). Robust Tracking With Motion Estimation And Local Kernel-Based
Color Modeling, Image and Vision Computing.
[8]Wu, Yi & et al, (2013). Online Object Tracking: A Benchmark, IEEE Conference on Computer Vision and
Pattern Recognition (CVPR).
[9] Porikli, F. , (2005). Multi-Kernel Object Tracking, IEEE International Conference on Multimedia and
Expo.
[10] Jeyakara, J. & VenkateshBabu, R. (2008). Robust Object Tracking with Background-Weighted Local
Kernels, Computer Vision and Image Understanding.
[11] Wesley, E. S. & Hairong, Q. (2004). Machine Vision, Cambridge University Press.
[12] Briechle, K. Template Maching using Fast Normalized Cross Corralation, Institude of Automatic Control
Engineering ,Technical University Munchen, Germany.
[13] The OpenCV Reference Manual, Release 2. 4. 3