Research on convolutional neural network for video classification

Yaru Zhou

doi:10.1117/12.2624727

13 December 2021 Research on convolutional neural network for video classification

Yaru Zhou

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871C (2021) https://doi.org/10.1117/12.2624727
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

Convolutional Neural Network (CNN) has recently been demonstrated as an effective model in machine learning for processing a large amount of data. In addition to its applications in natural language processing (NLP), CNN plays an important role in computer vision. This machine learning model can be applied to realize face recognition, image classification, image retrieval, object detection video classification, etc. In this paper, the author constructs an CNN model to classify videos based on the data set HMDB51. HMDB51 is a large human motion database, consisting of a total of 7,000 clips distributed in 51 action classes. Different from the 2D image, video is the three-dimensional data, including time and the width and height of each frame in the video. Therefore, it is necessary to process the video into a sequence of frames of uniform length and normalize the frames firstly. This would facilitate subsequent training and help prevent premature over-fitting as well. Besides, the impact of changing parameters in this CNN model would also be tested, such as the length of image frames, the number of sample categories, the batch size to training the data and the location of dropout layers. The results show that it is difficult to significantly improve the accuracy of recognition by adjusting these parameters alone, and those various factors need to be considered comprehensively

Citation Download Citation

Yaru Zhou "Research on convolutional neural network for video classification", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871C (13 December 2021); https://doi.org/10.1117/12.2624727

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KEYWORDS

Video

Convolution

Data modeling

Convolutional neural networks

Image retrieval

Image filtering

Image segmentation

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