Automatic objects detection and tracking using FPCP, Blob analysis and Kalman filter
Joint Authors
Abd al-Ghafur, Nuha H.
Abd Allah, Hadil Nasrat
Source
Engineering and Technology Journal
Issue
Vol. 38, Issue 2A (29 Feb. 2020), pp.246-254, 9 p.
Publisher
Publication Date
2020-02-29
Country of Publication
Iraq
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
Object detection and tracking are key mission in computer visibility applications, including civil or military surveillance systems.
However, there are major challenges that have an effective role in the accuracy of detection and tracking such as the ability of the system to track the target and the response speed of the system in different environments as well as the presence of noise in the captured video sequence.
In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP).
Then, we used an ideal filter that performs well to reduce noise through the morphological filter.
The Blob analysis is used to add smoothness to the spatial identification of objects and their areas, and finally, the detected object is tracked by Kalman Filter.
The applied examples demonstrated the efficiency and capability of the proposed system for noise removal, detection accuracy and tracking.
American Psychological Association (APA)
Abd Allah, Hadil Nasrat& Abd al-Ghafur, Nuha H.. 2020. Automatic objects detection and tracking using FPCP, Blob analysis and Kalman filter. Engineering and Technology Journal،Vol. 38, no. 2A, pp.246-254.
https://search.emarefa.net/detail/BIM-948902
Modern Language Association (MLA)
Abd Allah, Hadil Nasrat& Abd al-Ghafur, Nuha H.. Automatic objects detection and tracking using FPCP, Blob analysis and Kalman filter. Engineering and Technology Journal Vol. 38, no. 2A (2020), pp.246-254.
https://search.emarefa.net/detail/BIM-948902
American Medical Association (AMA)
Abd Allah, Hadil Nasrat& Abd al-Ghafur, Nuha H.. Automatic objects detection and tracking using FPCP, Blob analysis and Kalman filter. Engineering and Technology Journal. 2020. Vol. 38, no. 2A, pp.246-254.
https://search.emarefa.net/detail/BIM-948902
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 254
Record ID
BIM-948902