Performance of Deep Convolutional Neural Networks for Motion Detection in Video Frames
Publish place: 3rd International Conference on Soft Computing
Publish Year: 1398
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSCG03_121
تاریخ نمایه سازی: 14 فروردین 1399
Abstract:
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.
Keywords:
Convolutional Neural Network , Motion Detection , Hidden Layer , Fully Connected Layer , Rectified Linear Unit Layer , Convolutional Layer.
Authors
Zahra Ramezani
Department of Statistics, University of Mazandaran, Babolsar, Iran;
Ahmad Pourdarvish
Department of Statistics, University of Mazandaran, Babolsar, Iran;