چکیده مقاله
In this paper, the performance of deep Convolutional Neural Networks CNNs with the number of different layers has been applied to classify video frames The applied approach emphasizes on the health of workers and shows deep CNN architectures accurately learn features of objects as opposed to more shallow CNN architecture Finally, the results indicate that deeper convolutional neural network is more efficient and this method is useful when there are a lot of data available
کلیدواژهها
Convolutional Neural Network Motion Detection Hidden Layer Fully Connected Layer Convolutional Layer. Rectified Linear Unit Layer Convolutional Layer
نویسندگان
Zahra Ramezani
Department of Statistics, University of Mazandaran, Babolsar, Iran;
Ahmad Pourdarvish
Department of Statistics, University of Mazandaran, Babolsar, Iran;
شیوه ارجاع
Ramezani, Zahra and Pourdarvish, Ahmad,1398,Performance of Deep Convolutional Neural Networks for Motion Detection in Video Frames,3rd International Conference on Soft Computing,Rudsar
ارائهشده در
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