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.
A dynamic target detection method based on the fusion of optical flow and neural network is proposed.
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.
It provides a powerful safeguard for target detection and targets the tracking application.
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.
This work was supported by the National Natural Science Foundation of China (No. 61304223, No. 61673209 and No. 61533008) and the Fundamental Research Funds for the Central Universities (No. NZ2015206 and No. NJ20160026).
Qin, H., Zhen, Z. and Ma, K. (2016), "Moving object detection based on optical flow and neural network fusion", International Journal of Intelligent Computing and Cybernetics, Vol. 9 No. 4, pp. 325-335. https://doi.org/10.1108/IJICC-06-2016-0020Download as .RIS
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