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  • Open Access


    Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

    Yunfeng Cai1, Ran Qin1, Jin Tang1, Long Zhang1, Xiaotian Bi1, Qing Yang2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4979-4994, 2024, DOI:10.32604/cmc.2024.050435

    Abstract Electric power training is essential for ensuring the safety and reliability of the system. In this study, we introduce a novel Abnormal Action Recognition (AAR) system that utilizes a Lightweight Pose Estimation Network (LPEN) to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios. The LPEN network, comprising three stages—MobileNet, Initial Stage, and Refinement Stage—is employed to swiftly extract image features, detect human key points, and refine them for accurate analysis. Subsequently, a Pose-aware Action Analysis Module (PAAM) captures the positional coordinates of human skeletal points in each frame. Finally, More >

  • Open Access


    Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

    Arnab Dey1,*, Samit Biswas1, Dac-Nhuong Le2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3067-3087, 2024, DOI:10.32604/cmc.2024.049512

    Abstract Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers the likelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in video streams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enable instant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing action datasets often lack diversity and specificity for workout actions, hindering the development of accurate recognition models. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significant… More >

  • Open Access


    HgaNets: Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

    Wuyan Liang1, Xiaolong Xu2,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1089-1103, 2024, DOI:10.32604/cmc.2024.047861

    Abstract Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual and skeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data, failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility to efficiently process both uniform and disparate input patterns. Thus, in this paper, an attention-enhanced pseudo-3D residual model is proposed to address the GAR problem, called HgaNets. This model comprises two independent components designed for modeling visual RGB (red, green and blue) images and 3D skeletal heatmaps, respectively. More… More >

  • Open Access


    BCCLR: A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

    Yunhe Wang1, Yuxin Xia2, Shuai Liu2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4489-4507, 2024, DOI:10.32604/cmc.2024.048813

    Abstract In recent years, skeleton-based action recognition has made great achievements in Computer Vision. A graph convolutional network (GCN) is effective for action recognition, modelling the human skeleton as a spatio-temporal graph. Most GCNs define the graph topology by physical relations of the human joints. However, this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs, resulting in a low recognition rate for specific actions with implicit correlation between joint pairs. In addition, existing methods ignore the trend correlation between adjacent frames within an action… More >

  • Open Access


    TEAM: Transformer Encoder Attention Module for Video Classification

    Hae Sung Park1, Yong Suk Choi2,*

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 451-477, 2024, DOI:10.32604/csse.2023.043245

    Abstract Much like humans focus solely on object movement to understand actions, directing a deep learning model’s attention to the core contexts within videos is crucial for improving video comprehension. In the recent study, Video Masked Auto-Encoder (VideoMAE) employs a pre-training approach with a high ratio of tube masking and reconstruction, effectively mitigating spatial bias due to temporal redundancy in full video frames. This steers the model’s focus toward detailed temporal contexts. However, as the VideoMAE still relies on full video frames during the action recognition stage, it may exhibit a progressive shift in attention towards… More >

  • Open Access


    Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

    Oscar Peña-Cáceres1,2,*, Henry Silva-Marchan3, Manuela Albert4, Miriam Gil1

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1873-1891, 2023, DOI:10.32604/cmc.2023.043176

    Abstract The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in… More >

  • Open Access


    A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

    Tanvir Fatima Naik Bukht1, Naif Al Mudawi2, Saud S. Alotaibi3, Abdulwahab Alazeb2, Mohammed Alonazi4, Aisha Ahmed AlArfaj5, Ahmad Jalal1, Jaekwang Kim6,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1557-1573, 2023, DOI:10.32604/cmc.2023.041335

    Abstract Human-human interaction recognition is crucial in computer vision fields like surveillance, human-computer interaction, and social robotics. It enhances systems’ ability to interpret and respond to human behavior precisely. This research focuses on recognizing human interaction behaviors using a static image, which is challenging due to the complexity of diverse actions. The overall purpose of this study is to develop a robust and accurate system for human interaction recognition. This research presents a novel image-based human interaction recognition method using a Hidden Markov Model (HMM). The technique employs hue, saturation, and intensity (HSI) color transformation to… More >

  • Open Access


    Action Recognition for Multiview Skeleton 3D Data Using NTURGB + D Dataset

    Rosepreet Kaur Bhogal1,*, V. Devendran2

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2759-2772, 2023, DOI:10.32604/csse.2023.034862

    Abstract Human activity recognition is a recent area of research for researchers. Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety, monitor indoor and outdoor activities, develop Tele immersion systems, or detect abnormal activity recognition. Three dimensions (3D) skeleton data is robust and somehow view-invariant. Due to this, it is one of the popular choices for human action recognition. This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence. Further proposed two neural networks: convolutional neural network recurrent neural network_1… More >

  • Open Access


    Action Recognition and Detection Based on Deep Learning: A Comprehensive Summary

    Yong Li1,4, Qiming Liang2,*, Bo Gan3, Xiaolong Cui4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1-23, 2023, DOI:10.32604/cmc.2023.042494

    Abstract Action recognition and detection is an important research topic in computer vision, which can be divided into action recognition and action detection. At present, the distinction between action recognition and action detection is not clear, and the relevant reviews are not comprehensive. Thus, this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field. Firstly, according to the way that temporal and spatial features are extracted from the model, the commonly used models of action recognition are divided into the two stream… More >

  • Open Access


    HybridHR-Net: Action Recognition in Video Sequences Using Optimal Deep Learning Fusion Assisted Framework

    Muhammad Naeem Akbar1,*, Seemab Khan2, Muhammad Umar Farooq1, Majed Alhaisoni3, Usman Tariq4, Muhammad Usman Akram1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3275-3295, 2023, DOI:10.32604/cmc.2023.039289

    Abstract The combination of spatiotemporal videos and essential features can improve the performance of human action recognition (HAR); however, the individual type of features usually degrades the performance due to similar actions and complex backgrounds. The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information. This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net. On a few selected datasets, deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model. Bayesian optimization is employed for the tuning of… More >

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