Author(s)

Ihar Yeuseyenka^1*, Ihar Melnikau², Ihar Yemelyanov³

¹Research Center of Automotive Power Train and Electronic Control, College of Automotive Engineering, Hubei University of Automotive Technology, Shiyan, China.
²Department of Asset Performance and Information Management, AVEVA Solutions Ltd., Cambridge, The United Kingdom.
³Laboratory of Mathematical Cybernetics, United Institute of Informatics Problems, Minsk, Belarus.

The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simulation. The result of the work is a developed motion detector based on impulse and recurrence neural networks and an automated system developed on the basis of this detector for detecting and separating moving objects and is ready for practical application. The feasibility of integrating the developed motion detector with Emgu CV (OpenCV) image processing package, multimedia framework functions, and DirectShow application programming interface were investigated. The proposed approach and software for the detection and separating of moving objects in video images using neural networks can be integrated into more sophisticated specialized computer-aided video surveillance systems, IoT (Internet of Things), IoV (Internet of Vehicles), etc.

Automated System, Video Image, Pixel, Neuron, Neural Network

Yeuseyenka, I. , Melnikau, I. and Yemelyanov, I. (2022) Detection and Selection of Moving Objects in Video Images Based on Impulse and Recurrent Neural Networks. Journal of Data Analysis and Information Processing, 10, 127-141. doi: 10.4236/jdaip.2022.102008.