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  • Open Access


    Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

    Qingyue Zhao1, Qiaoyu Gu2, Zhijun Gao3,*, Shipian Shao1, Xinyuan Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1773-1788, 2023, DOI:10.32604/cmes.2023.027500

    Abstract Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, an attention layer is designed… More >

  • Open Access


    Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

    Min-Jeong Kim1, Byeong-Uk Jeon1, Hyun Yoo2, Kyungyong Chung3,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2371-2386, 2023, DOI:10.32604/iasc.2023.040310

    Abstract With the increasing number of digital devices generating a vast amount of video data, the recognition of abnormal image patterns has become more important. Accordingly, it is necessary to develop a method that achieves this task using object and behavior information within video data. Existing methods for detecting abnormal behaviors only focus on simple motions, therefore they cannot determine the overall behavior occurring throughout a video. In this study, an abnormal behavior detection method that uses deep learning (DL)-based video-data structuring is proposed. Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models. The… More >

  • Open Access


    Improved Transient Search Optimization with Machine Learning Based Behavior Recognition on Body Sensor Data

    Baraa Wasfi Salim1, Bzar Khidir Hussan2, Zainab Salih Ageed3, Subhi R. M. Zeebaree4,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4593-4609, 2023, DOI:10.32604/cmc.2023.037514

    Abstract Recently, human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems. Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs, physical human action recognition (HAR) through body sensors gives useful information about the lifestyle and functionality of an individual. This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based Behavior Recognition (ITSOML-BR) technique using body sensor data. The presented ITSOML-BR technique collects data from… More >

  • Open Access


    Sika Deer Behavior Recognition Based on Machine Vision

    He Gong1,3,4, Mingwang Deng1, Shijun Li1,2,6,*, Tianli Hu1,3,4, Yu Sun1,3,4, Ye Mu1,3,4, Zilian Wang1, Chang Zhang1, Thobela Louis Tyasi5

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4953-4969, 2022, DOI:10.32604/cmc.2022.027457

    Abstract With the increasing intensive and large-scale development of the sika deer breeding industry, it is crucial to assess the health status of the sika deer by monitoring their behaviours. A machine vision–based method for the behaviour recognition of sika deer is proposed in this paper. Google Inception Net (GoogLeNet) is used to optimise the model in this paper. First, the number of layers and size of the model were reduced. Then, the 5 × 5 convolution was changed to two 3 × 3 convolutions, which reduced the parameters and increased the nonlinearity of the model. A 5 × 5 convolution… More >

  • Open Access


    Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model

    Huifang Qian1, Xuan Zhou1, *, Mengmeng Zheng1

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2153-2167, 2020, DOI:10.32604/cmc.2020.011843

    Abstract The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately. The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture, so as to solve the problem of recognizing them. In response to this difficulty, this paper introduces an adjustable jump link coefficients model based on the residual network. The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior. A convolution kernel of 1×1 size is added to reduce… More >

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