IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Moving Target Detection and Tracking Based on Improved FCM Algorithm

Moving Target Detection and Tracking Based on Improved FCM Algorithm
View Sample PDF
Author(s): Wang Ke Feng (Jiangsu University of Technology, Changzhou, China) and Sheng Xiao Chun (Jiangsu University of Technology, Changzhou, China)
Copyright: 2020
Volume: 14
Issue: 1
Pages: 12
Source title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)
Editor(s)-in-Chief: Kangshun Li (South China Agricultural University, China)
DOI: 10.4018/IJCINI.2020010105

Purchase

View Moving Target Detection and Tracking Based on Improved FCM Algorithm on the publisher's website for pricing and purchasing information.

Abstract

With the rapid development of computer intelligence technology, the majority of scholars have a great interest in the detection and tracking of moving targets in the field of video surveillance and have been involved in its research. Moving target detection and tracking has also been widely used in military, industrial control, and intelligent transportation. With the rapid progress of the social economy, the supervision of traffic has become more and more complicated. How to detect the vehicles on the road in real time, monitor the illegal vehicles, and control the illegal vehicles effectively has become a hot issue. In view of the complex situation of moving vehicles in various traffic videos, the authors propose an improved algorithm for effective detection and tracking of moving vehicles, namely improved FCM algorithm. It combines traditional FCM algorithm with genetic algorithm and Kalman filter algorithm to track and detect moving targets. Experiments show that this improved clustering algorithm has certain advantages over other clustering algorithms.

Related Content

Alae Chouiekh, El Hassane Ibn El Haj. © 2020. 16 pages.
Maryam Ghanbari, Witold Kinsner. © 2020. 18 pages.
Adnen Mahmoud, Mounir Zrigui. © 2020. 16 pages.
Jun Ye. © 2020. 12 pages.
Wang Ke Feng, Sheng Xiao Chun. © 2020. 12 pages.
Ying Huang, Liyun Zhong, Yan Chen. © 2020. 15 pages.
Gaurav Aggarwal, Latika Singh. © 2020. 19 pages.
Body Bottom