Moving object detection based on optical flow and neural network fusion

作者：Qin Haiqun; Zhen Ziyang^*; Ma Kun

来源：International Journal of Intelligent Computing and Cybernetics, 2016, 9(4): 325-335.

DOI：10.1108/IJICC-06-2016-0020

• Design/methodology/approach
• Dynamic background
• Dynamic target detection
• Intelligent monitoring systems
• Monitoring system
• Moving-object detection
• Neural network fusions
• Tracking application

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 background. Design/methodology/approach: A dynamic target detection method based on the fusion of optical flow and neural network is proposed. Findings: Simulation results verify the accuracy of the moving object detection based on optical flow and neural network fusion. The method eliminates the influence caused by the movement of the camera to detect the target and has the ability to extract a complete moving target. Practical implications: It provides a powerful safeguard for target detection and targets the tracking application. Originality/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.

• 出版日期2016
• 单位南京航空航天大学