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  • Open Access

    ARTICLE

    Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition

    Motasem S. Alsawadi1,*, El-Sayed M. El-kenawy2, Miguel Rio1

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2767-2782, 2023, DOI:10.32604/iasc.2023.039440

    Abstract The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph’s topology and then fix it across… More >

  • Open Access

    ARTICLE

    SlowFast Based Real-Time Human Motion Recognition with Action Localization

    Gyu-Il Kim1, Hyun Yoo2, Kyungyong Chung3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2135-2152, 2023, DOI:10.32604/csse.2023.041030

    Abstract Artificial intelligence is increasingly being applied in the field of video analysis, particularly in the area of public safety where video surveillance equipment such as closed-circuit television (CCTV) is used and automated analysis of video information is required. However, various issues such as data size limitations and low processing speeds make real-time extraction of video data challenging. Video analysis technology applies object classification, detection, and relationship analysis to continuous 2D frame data, and the various meanings within the video are thus analyzed based on the extracted basic data. Motion recognition is key in this analysis.… More >

  • Open Access

    ARTICLE

    MSF-Net: A Multilevel Spatiotemporal Feature Fusion Network Combines Attention for Action Recognition

    Mengmeng Yan1, Chuang Zhang1,2,*, Jinqi Chu1, Haichao Zhang1, Tao Ge1, Suting Chen1

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1433-1449, 2023, DOI:10.32604/csse.2023.040132

    Abstract An action recognition network that combines multi-level spatiotemporal feature fusion with an attention mechanism is proposed as a solution to the issues of single spatiotemporal feature scale extraction, information redundancy, and insufficient extraction of frequency domain information in channels in 3D convolutional neural networks. Firstly, based on 3D CNN, this paper designs a new multilevel spatiotemporal feature fusion (MSF) structure, which is embedded in the network model, mainly through multilevel spatiotemporal feature separation, splicing and fusion, to achieve the fusion of spatial perceptual fields and short-medium-long time series information at different scales with reduced network… More >

  • Open Access

    ARTICLE

    Fine-Grained Action Recognition Based on Temporal Pyramid Excitation Network

    Xuan Zhou1,*, Jianping Yi2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2103-2116, 2023, DOI:10.32604/iasc.2023.034855

    Abstract Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recognition. Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation. However, these methods failed to capture complex motion patterns due to their limited receptive field. To solve the above problems, this paper proposes a lightweight Temporal Pyramid Excitation (TPE) module to capture the short, medium, and long-term temporal context. In this method, Temporal Pyramid (TP) module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without More >

  • Open Access

    ARTICLE

    A Novel Computationally Efficient Approach to Identify Visually Interpretable Medical Conditions from 2D Skeletal Data

    Praveen Jesudhas1,*, T. Raghuveera2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2995-3015, 2023, DOI:10.32604/csse.2023.036778

    Abstract Timely identification and treatment of medical conditions could facilitate faster recovery and better health. Existing systems address this issue using custom-built sensors, which are invasive and difficult to generalize. A low-complexity scalable process is proposed to detect and identify medical conditions from 2D skeletal movements on video feed data. Minimal set of features relevant to distinguish medical conditions: AMF, PVF and GDF are derived from skeletal data on sampled frames across the entire action. The AMF (angular motion features) are derived to capture the angular motion of limbs during a specific action. The relative position… More >

  • Open Access

    ARTICLE

    HRNetO: Human Action Recognition Using Unified Deep Features Optimization Framework

    Tehseen Ahsan1,*, Sohail Khalid1, Shaheryar Najam1, Muhammad Attique Khan2, Ye Jin Kim3, Byoungchol Chang4

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1089-1105, 2023, DOI:10.32604/cmc.2023.034563

    Abstract Human action recognition (HAR) attempts to understand a subject’s behavior and assign a label to each action performed. It is more appealing because it has a wide range of applications in computer vision, such as video surveillance and smart cities. Many attempts have been made in the literature to develop an effective and robust framework for HAR. Still, the process remains difficult and may result in reduced accuracy due to several challenges, such as similarity among actions, extraction of essential features, and reduction of irrelevant features. In this work, we proposed an end-to-end framework using… More >

  • Open Access

    ARTICLE

    Two-Stream Deep Learning Architecture-Based Human Action Recognition

    Faheem Shehzad1, Muhammad Attique Khan2, Muhammad Asfand E. Yar3, Muhammad Sharif1, Majed Alhaisoni4, Usman Tariq5, Arnab Majumdar6, Orawit Thinnukool7,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5931-5949, 2023, DOI:10.32604/cmc.2023.028743

    Abstract Human action recognition (HAR) based on Artificial intelligence reasoning is the most important research area in computer vision. Big breakthroughs in this field have been observed in the last few years; additionally, the interest in research in this field is evolving, such as understanding of actions and scenes, studying human joints, and human posture recognition. Many HAR techniques are introduced in the literature. Nonetheless, the challenge of redundant and irrelevant features reduces recognition accuracy. They also faced a few other challenges, such as differing perspectives, environmental conditions, and temporal variations, among others. In this work,… More >

  • Open Access

    ARTICLE

    Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

    Chih-Ta Yen1,*, Tz-Yun Chen2, Un-Hung Chen3, Guo-Chang Wang3, Zong-Xian Chen3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 83-99, 2023, DOI:10.32604/cmc.2023.032739

    Abstract A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors More >

  • Open Access

    ARTICLE

    Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

    Motasem S. Alsawadi1,*, El-Sayed M. El-kenawy2,3, Miguel Rio1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 19-36, 2023, DOI:10.32604/cmc.2023.032499

    Abstract The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community's attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of More >

  • Open Access

    ARTICLE

    Motion Enhanced Model Based on High-Level Spatial Features

    Yang Wu1, Lei Guo1, Xiaodong Dai1, Bin Zhang1, Dong-Won Park2, Ming Ma1,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5911-5924, 2022, DOI:10.32604/cmc.2022.031664

    Abstract Action recognition has become a current research hotspot in computer vision. Compared to other deep learning methods, Two-stream convolutional network structure achieves better performance in action recognition, which divides the network into spatial and temporal streams, using video frame images as well as dense optical streams in the network, respectively, to obtain the category labels. However, the two-stream network has some drawbacks, i.e., using dense optical flow as the input of the temporal stream, which is computationally expensive and extremely time-consuming for the current extraction algorithm and cannot meet the requirements of real-time tasks. In… More >

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