RBF神经网络的多特征融合目标跟踪算法*

PDF(1323 KB)

集成电路与嵌入式系统 ›› 2022, Vol. 22 ›› Issue (3) : 32-35.

专题论述

RBF神经网络的多特征融合目标跟踪算法^*

刘英明

作者信息 +

Multi-feature Fusion Object Tracking by RBF Neural Networks

Liu Yingming

Author information +

文章历史 +

摘要

本文提出一种基于RBF神经网络的多特征融合目标跟踪算法。RBF神经网络是一种简单且高效的三层神经网络,可以大大提高跟踪效率。首先利用引入空间相关性的三维颜色直方图、Canny算子以及灰度图多个特征来构造RBF神经网络的输入特征向量;然后,采用三角核函数作为RBF神经网络的激活函数;最后,利用所提算法对目标进行跟踪。实验结果表明,所提算法能够对目标进行可靠跟踪,对相机移动、光照变化、目标旋转、形状变形等问题有很好的适应性。

Abstract

In the paper,a multi-feature fusion target tracking algorithm based on RBF neural network is proposed.RBF neural network is a simple and efficient three-layer neural network,which can greatly improve tracking efficiency.This paper first uses the three-dimensional color histogram,the Canny operator and the gray-scale image with spatial correlation to construct the input feature vector of the RBF neural network.Then use the triangular kernel function as the activation function of the RBF neural network.Finally,the proposed algorithm is used to track the target.The experiment results show that the target tracking algorithm proposed in this paper can track the target reliably,and has good adaptability to problems such as camera movement,illumination changes,target rotation,and shape deformation.

导出引用

刘英明. RBF神经网络的多特征融合目标跟踪算法^*[J]. 集成电路与嵌入式系统. 2022, 22(3): 32-35

Liu Yingming. Multi-feature Fusion Object Tracking by RBF Neural Networks[J]. Integrated Circuits and Embedded Systems. 2022, 22(3): 32-35

中图分类号： TP391

参考文献

[1] 尹宏鹏,陈波,柴毅,等. 基于视觉的目标检测与跟踪综述 [J].自动化学报,2016,42(10):1466-1489.
[2] 陈彦明,赵清杰,刘若宇.一种适用于分布式摄像机网络的SCIWCF算法[J].电子学报,2016,44(10):2335-2343.
[3] 顾幸方,茅耀斌,李秋洁.基于Mean Shift的视觉目标跟踪算法综述[J].计算机科学,2012,39(12):16-24.
[4] 赖程程,梁麟.基于轮廓模型和AdaBoost算法的民族地区舞蹈人员跟踪技术研究[J].现代电子技术,2020,43(5):42-45.
[5] 管皓,薛向阳,安志勇.深度学习在视频目标跟踪中的应用进展与展望[J].自动化学报,2016,42(6):834-847.
[6] 单超颖,李权,郭莉莉.RBF神经网络优化后的无线网络室内定位 [J].现代电子技术,2020,43(22):49-52,56.
[7] 王文成,邱胜朋,姚金峰,等.RBF模型预测PID在两级AO污水处理中的改进研究[J].现代电子技术,2020,43(3):104-108.
[8] BABU R V,SURESH S,MAKURA.Online adaptive radial basis function networks for robust object tracking[J].Computer Vision and Image Understanding,2010,114(3):297-310.
[9] 尹钊,贾尚晖.Moore-Penrose广义逆矩阵与线性方程组的解[J]. 数学的实践与认识,2009,39(9):239-244.
[10] 曾欢,王浩.图像边缘检测算法的性能比较与分析[J].现代电子技术,2006(14):53-55,58.
[11] WU Y,LIM J,YANG M H.Online object tracking:A benchmark;proceedings of the 2013 IEEE[C]//Conference on Computer Vision and Pattern Recognition,Portland,OR,USA,2013.