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Double Channel 3D Convolutional Neural Network for Exam Scene Classification of Invigilation Videos

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成果类型:
期刊论文、会议论文
作者:
Song, Wu*;Yu, Xinguo(余新国
通讯作者:
Song, Wu
作者机构:
[Yu, Xinguo; Song, Wu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构:
[Song, Wu] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Exam invigilation video;Video scene classification;Convolutional neural network
期刊:
Lecture Notes in Computer Science
ISSN:
0302-9743
年:
2019
卷:
11854
页码:
116-127
会议名称:
9th Pacific-Rim Symposium on Image and Video Technology (PSIVT)
会议论文集名称:
Lecture Notes in Computer Science
会议时间:
NOV 18-22, 2019
会议地点:
Sydney, AUSTRALIA
会议主办单位:
[Song, Wu;Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
主编:
Lee, C Su, Z Sugimoto, A
出版地:
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
出版者:
SPRINGER INTERNATIONAL PUBLISHING AG
ISBN:
978-3-030-34879-3; 978-3-030-34878-6
基金类别:
General Program of the National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61977029]
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
This paper presents a double channel 3D convolution neural network to classify the exam scenes of invigilation videos. The first channel is based on the C3D convolution neural network, which is the status-of-arts method of the video scene classification. The structure of this channel is redesigned for classifying the exam-room scenes of invigilation videos. Another channel is based on the two-stream convolution neural network using the optical flow graph sequence as its input. This channel uses the data from the optical flow of video to improve the performance of the video scene classification...

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