计算机科学 ›› 2021, Vol. 48 ›› Issue (8): 246-252.doi: 10.11896/jsjkx.200600050

• 人工智能 • 上一篇    下一篇

基于模糊神经网络的运动目标智能分配定位算法

屈立成, 吕娇, 屈艺华, 王海飞   

  1. 长安大学信息工程学院 西安710064
  • 收稿日期:2020-06-08 修回日期:2020-09-15 发布日期:2021-08-10
  • 通讯作者: 吕娇(2018124058@chd.edu.cn)
  • 基金资助:
    陕西省自然科学基础研究计划资助项目(2020JM-258);国家重点研发计划(2018YFB1601004)

Intelligent Assignment and Positioning Algorithm of Moving Target Based on Fuzzy Neural Network

QU Li-cheng, LYU Jiao, QU Yi-hua, WANG Hai-fei   

  1. School of Information Engineering,Chang'an University,Xi'an 710064,China
  • Received:2020-06-08 Revised:2020-09-15 Published:2021-08-10
  • About author:QU Li-cheng,born in 1976,Ph.D,senior engineer.His main research interests include human intelligence and big data,intelligent transportation system.(qlc@chd.edu.cn)LYU Jiao,born in 1996,postgraduate.Her main research interests include intelligent video surveillance and so on.
  • Supported by:
    Natural Science Basic Research of Shaanxi(2020JM-258) and National Key Research and Development Program of China(2018YFB1601004).

摘要: 为解决特殊应用场景下智能视频监控系统存在的监测范围有限、监控资源分配不合理、运动目标发现不及时等问题,利用雷达探测电磁波穿透能力强、搜索范围大、不受特殊气候及光学条件影响的特点,结合无人机和自动导航小车的灵活性和机动性,提出了一种雷达指引的综合联动视频监控方法,并在此基础上研究了一种基于大地坐标的统一坐标定位体系以及使用粒子群优化算法进行优化的基于模糊神经网络的运动目标智能分配定位算法。该算法根据雷达探测信号可自动计算出每台摄像机在水平、垂直和变焦3个维度上的控制参数,结合联动控制系统实现运动目标的实时定位和追踪。通过对某文物保护现场的实地测试,大地坐标定位体系的目标定位精度达到了99.84%,基于模糊神经网络的运动目标智能分配算法的准确率达到了95%,能够实现目标的精准定位和监控资源的智能分配,具有较高的实际应用价值。

关键词: 粒子群优化算法, 模糊神经网络, 目标定位, 智能分配, 智能视频监控

Abstract: In order to solve the problems of limited monitoring range,unreasonable allocation of monitoring resources,and untimely detection of moving targets in intelligent video surveillance systems under special application scenarios,the use of radar to detect electromagnetic waves has astrong penetration ability,large search range,and being not subject to special weather and optical conditions.Combined with the flexibility and maneuverability of unmanned aerial vehicles and automatic navigation vehicles,this paper proposes a radar-directed integrated linkage video surveillance model,and on this basis,studies a unified coordinate positioning system based on geodetic coordinates and intelligent assignment and positioning algorithm of moving target based on fuzzy neural network optimized by particle swarm optimization.The algorithm can automatically solve the control parameters of each camera in three dimensions of horizontal,vertical and zoom according to the radar detection signal,and combines the linkage control system to achieve real-time positioning and tracking of moving targets.Through field tests at a cultural relics protection site,the accuracy of the target positioning accuracy of the geodetic positioning system reaches 99.6%,and the accuracy rate of the intelligent assignment algorithm for moving targets based on the fuzzy neural network reaches 95%,which can achieve precise positioning and intelligent allocation of monitoring resources,and has high practical application value.

Key words: Fuzzy neural network, Intelligent assignment, Intelligent video surveillance, Particle swarm optimization, Target positioning

中图分类号: 

  • TP181
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