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Moving Target Detection and Tracking Based on Improved FCM Algorithm

Wang Ke Feng, Sheng Xiao Chun

Source Title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 14(1)

DOI: 10.4018/IJCINI.2020010105

Article PDF Download Open access articles are freely available for download

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.

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2. Fcm Clustering Algorithm

The fuzzy C-means clustering algorithm (FCM) is simple in principle, easy to be operated and widely used. The core idea is to minimize the objective function, and to find the final class center and membership matrix. A given data set , which contains N samples, the number of clusters is C, is the degree of membership for the first j sample which belong to the Ith category , the objective function of FCM is as follows[Gao, 2016]: