Abstract
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. The formed double channel 3D convolution neural network has appropriate size of convolution kernel and pooling kernel design. Experiments show that the proposed neural network can classify the exam-room scenes of invigilation videos faster and more accurately than the existing methods.
This study is funded by the General Program of the National Natural Science Foundation of China (No: 61977029).
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Song, W., Yu, X. (2019). Double Channel 3D Convolutional Neural Network for Exam Scene Classification of Invigilation Videos. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_10
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DOI: https://doi.org/10.1007/978-3-030-34879-3_10
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