Effective Moving Object Tracking Using Adaptive Background Subtraction with Advanced Probability Evolutionary Algorithm

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Published: Aug 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9s.7389
Keywords:
Moving objects, Tracking, Detection, APEA

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Adinarayana Ekkurthi
Dept .of Elctronics and Communication Engineering Sri Mittapalli College of Engineering Tummalapalem, NH-16, Guntur,A.P,India
V. Sujatha
Dept .of Computer Science and Engineering Vignan’s Nirula Institute of Tecnology and Science for Women Guntur, A.P,India
K. Vijay Kumar
Dept .of Elctronics and Communication Engineering Sri Mittapalli College of Engineering Tummalapalem, NH-16, Guntur,A.P,India

Abstract

Tracking moving objects is a very difficult task for many computers. The task of tracking is a moving object. There are many virtual applications such as video surveillance and object recognition. Many ideas have been suggested for moving objects tracking and detection. One of the main problems solving tracking is finding identical objects in different frames. In this article, this application mainly focuses on the detection and tracking of moving objects using adaptive Background subtraction with adaptive probability evolutionary algorithm (AREA) method. Normal probability evolutionary algorithm will track only human motion but our algorithm tracks all the moving objects very accurately. Our proposed system shows the tracked objects with the red mark border. 

Article Details

How to Cite
Ekkurthi, A. ., Sujatha, V., & Kumar, K. V. . (2023). Effective Moving Object Tracking Using Adaptive Background Subtraction with Advanced Probability Evolutionary Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 01–03. https://doi.org/10.17762/ijritcc.v11i9s.7389
Issue
Vol. 11 No. 9s (2023)
Section
Articles

References

R. Cucchiara, C. Grana, G. Neri, M. Piccardi and A.Prati, “The Sakbot system for moving object detection and tracking,” Video-based Surveillance Systems-Computer vision and Distributed Processing, pp. 145-157, 2001.

C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1999I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.

Solanki, S. ., Singh, U. P. ., Chouhan, S. S. ., & Jain, S. . (2023). Brain Tumour Detection and Classification by using Deep Learning Classifier. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 279 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2624

C. Wren, A. Azarbayejani, T. Darrell, A. Pentl, “Pfinder: Real-time tracking of the human body,” In IEEE Trans. Pattern Analysis and Machine Intelligent, vol. 19, no. 7, pp. 780785.

L. Qiu and L. Li, “Contour extraction of moving objects,” in Proc. IEEE Int’l Conf. Pattern Recognition, vol. 2, 1998, pp. 1427–1432.

Tang Sze Ling, Liang Kim Meng, Lim Mei Kuan, Zulaikha Kadim and Ahmed A. Baha‘a Al-Deen, “Colourbased Object Tracking in Surveillance Application” in Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 Vol I IMECS 2009, March 18 - 20, 2009, Hong Kong.

Haritaoglu I., Harwood D., and Davis L.S. 1998. W4S: “A real-time system for detecting and tracking people in 2 1/2D”, 5th European Conference on Computer Vision, Freiburg, Germany.