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    An Improved Two-stream Inflated 3D ConvNet for Abnormal Behavior Detection

    Jiahui Pan1,2,*, Liangxin Liu1, Mianfen Lin1, Shengzhou Luo1, Chengju Zhou1, Huijian Liao3, Fei Wang1,2

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 673-688, 2021, DOI:10.32604/iasc.2021.020240

    Abstract Abnormal behavior detection is an essential step in a wide range of application domains, such as smart video surveillance. In this study, we proposed an improved two-stream inflated 3D ConvNet network approach based on probability regression for abnormal behavior detection. The proposed approach consists of four parts: (1) preprocessing pretreatment for the input video; (2) dynamic feature extraction from video streams using a two-stream inflated 3D (I3D) ConvNet network; (3) visual feature transfer into a two-dimensional matrix; and (4) feature classification using a generalized regression neural network (GRNN), which ultimately achieves a probability regression. Compared with the traditional methods, two-stream… More >

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