[1]LIU Dongjingdian,MENG Xuechun,ZHANG Zixin,et al.A behavioral recognition algorithm based on 2D spatiotemporal information extraction[J].CAAI Transactions on Intelligent Systems,2020,15(5):900-909.[doi:10.11992/tis.201906054]
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A behavioral recognition algorithm based on 2D spatiotemporal information extraction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 900-909 Column: 学术论文—机器学习 Public date: 2020-09-05
Title:
A behavioral recognition algorithm based on 2D spatiotemporal information extraction
Author(s):
LIU Dongjingdian;  MENG Xuechun;  ZHANG Zixin;  YANG Xu;  NIU Qiang
College of Computer Science & Technology, China University of Mining and Technology , Xuzhou 221008, China
Keywords:
behavior recognition; video analysis; neural networks; deep learning; convolutional neural networks; classification; spatiotemporal feature; densenet
CLC:
TP391.41
DOI:
10.11992/tis.201906054
Abstract:
Human behavior recognition technology based on computer vision is a research hotspot currently. It is widely applied in various fields of social life, such as behavioral detection, video surveillance, etc. Traditional behavior recognition methods are computationally cumbersome and time-sensitive. Therefore, the development of deep learning has greatly improved the accuracy of behavior recognition algorithms. However, compared with the field of image processing, there is a certain gap in the effect of such methods. We introduce a novel behavior recognition algorithm based on DenseNet, which uses DenseNet as the network architecture, learns spatio-temporal information through 2D convolution, selects frames for characterizing behavior in video, organizes these frames into RGB space in time-space order and inputs them into our network to train the network. We have carried out a large number experiments on the UCF101 dataset, and our method can reach an accuracy rate of 94.46%.
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