Moving object detection based on optical flow and neural network fusion

作     者：Haiqun Qin Ziyang Zhen Kun Ma 

作者机构：College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina 

出 版 物：《International Journal of Intelligent Computing and Cybernetics》 (智能计算与控制论国际期刊（英文）)

年 卷 期：2016年第9卷第4期

页      面：325-335页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Natural Science Foundation of China(No.61304223,No.61673209 and No.61533008) the Fundamental Research Funds for the Central Universities(No.NZ2015206 and No.NJ20160026) 

主　　题：Neural network Fusion Moving object detection Optical flow 

摘      要：Purpose-The purpose of this paper is to meet the large demand for the new-generation intelligence monitoring systems that are used to detect targets within a dynamic ***/methodology/approach-A dynamic target detection method based on the fusion of optical flow and neural network is ***-Simulation results verify the accuracy of the moving object detection based on optical flow andneural network *** eliminates the influence caused bythe movement of thecamera to detect the target and has the ability to extract a complete moving *** implications-It provides a powerful safeguard for target detection and targets the tracking ***/value-The proposed method represents the fusion of optical flow and neural network to detect the moving object,and it can be used in new-generation intelligent monitoring systems.