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Moving Target Detection and Tracking Based on Improved FCM Algorithm

机译:基于改进FCM算法的运动目标检测与跟踪

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摘要

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.
机译:随着计算机智能技术的飞速发展,大多数学者对视频监视领域中移动目标的检测和跟踪产生了浓厚的兴趣,并参与了其研究。运动目标检测和跟踪也已广泛用于军事,工业控制和智能交通。随着社会经济的飞速发展,交通的监管越来越复杂。如何实时检测道路上的车辆,监控违章车辆,有效控制违章车辆已成为一个热门问题。针对各种交通视频中行驶车辆的复杂情况,作者提出了一种改进的有效检测和跟踪行驶车辆的算法,即改进的FCM算法。它结合了传统的FCM算法,遗传算法和卡尔曼滤波算法来跟踪和检测运动目标。实验表明,与其他聚类算法相比,改进后的聚类算法具有一定的优势。

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