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Object Recognition in Videos by Sequential Frame Extraction using Convolutional Neural Networks and Fully Connected Neural Networks

机译:使用卷积神经网络和完全连接的神经网络顺序帧提取通过顺序帧提取对象识别

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摘要

In this paper, a method to develop an interactive application in order to detect objects from videos is proposed. The application is able to classify the video according to a particular genre. Also, upon user input, it is also able to detect the particular object being shown at that instant on the screen. A sequential frame extraction method of videos and also deep learning approach of Convolutional Neural Networks along with Fully Connected Neural Networks is used for this task. The method gives good accuracy of average 77 percent.
机译:在本文中,提出了一种开发交互式应用程序的方法,以便检测来自视频的对象。应用程序能够根据特定类型对视频进行分类。此外,在用户输入时,它还能够检测在屏幕上瞬间显示的特定对象。卷积神经网络的视频和深度学习方法以及完全连接的神经网络的顺序帧提取方法用于此任务。该方法的良好精度平均为77%。

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