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Search Results (18)
  • Open Access


    Movement Function Assessment Based on Human Pose Estimation from Multi-View

    Lingling Chen1,2,*, Tong Liu1, Zhuo Gong1, Ding Wang1

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 321-339, 2024, DOI:10.32604/csse.2023.037865

    Abstract Human pose estimation is a basic and critical task in the field of computer vision that involves determining the position (or spatial coordinates) of the joints of the human body in a given image or video. It is widely used in motion analysis, medical evaluation, and behavior monitoring. In this paper, the authors propose a method for multi-view human pose estimation. Two image sensors were placed orthogonally with respect to each other to capture the pose of the subject as they moved, and this yielded accurate and comprehensive results of three-dimensional (3D) motion reconstruction that helped capture their multi-directional poses.… More >

  • Open Access


    Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

    Sangeeta Yadav1, Preeti Gulia1,*, Nasib Singh Gill1,*, Piyush Kumar Shukla2, Arfat Ahmad Khan3, Sultan Alharby4, Ahmed Alhussen4, Mohd Anul Haq5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1253-1274, 2024, DOI:10.32604/cmc.2023.042869

    Abstract Inpatient falls from beds in hospitals are a common problem. Such falls may result in severe injuries. This problem can be addressed by continuous monitoring of patients using cameras. Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient. Along with fall detection, monitoring of different activities of the patients is also of significant concern to assess the improvement in their health. High computation-intensive models are required to monitor every action of the patient precisely. This requirement limits the applicability of such networks. Hence, to keep the model lightweight, the already designed… More >

  • Open Access


    Lightweight Multi-Resolution Network for Human Pose Estimation

    Pengxin Li1, Rong Wang1,2,*, Wenjing Zhang1, Yinuo Liu1, Chenyue Xu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2239-2255, 2024, DOI:10.32604/cmes.2023.030677

    Abstract Human pose estimation aims to localize the body joints from image or video data. With the development of deep learning, pose estimation has become a hot research topic in the field of computer vision. In recent years, human pose estimation has achieved great success in multiple fields such as animation and sports. However, to obtain accurate positioning results, existing methods may suffer from large model sizes, a high number of parameters, and increased complexity, leading to high computing costs. In this paper, we propose a new lightweight feature encoder to construct a high-resolution network that reduces the number of parameters… More >

  • Open Access


    Full Scale-Aware Balanced High-Resolution Network for Multi-Person Pose Estimation

    Shaohua Li, Haixiang Zhang*, Hanjie Ma, Jie Feng, Mingfeng Jiang

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3379-3392, 2023, DOI:10.32604/cmc.2023.041538

    Abstract Scale variation is a major challenge in multi-person pose estimation. In scenes where persons are present at various distances, models tend to perform better on larger-scale persons, while the performance for smaller-scale persons often falls short of expectations. Therefore, effectively balancing the persons of different scales poses a significant challenge. So this paper proposes a new multi-person pose estimation model called FSA Net to improve the model’s performance in complex scenes. Our model utilizes High-Resolution Network (HRNet) as the backbone and feeds the outputs of the last stage’s four branches into the DCB module. The dilated convolution-based (DCB) module employs… More >

  • Open Access


    A Survey on Deep Learning-Based 2D Human Pose Estimation Models

    Sani Salisu1,2, A. S. A. Mohamed1,*, M. H. Jaafar3, Ainun S. B. Pauzi1, Hussain A. Younis1,4

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2385-2400, 2023, DOI:10.32604/cmc.2023.035904

    Abstract In this article, a comprehensive survey of deep learning-based (DL-based) human pose estimation (HPE) that can help researchers in the domain of computer vision is presented. HPE is among the fastest-growing research domains of computer vision and is used in solving several problems for human endeavours. After the detailed introduction, three different human body modes followed by the main stages of HPE and two pipelines of two-dimensional (2D) HPE are presented. The details of the four components of HPE are also presented. The keypoints output format of two popular 2D HPE datasets and the most cited DL-based HPE articles from… More >

  • Open Access


    3D Human Pose Estimation Using Two-Stream Architecture with Joint Training

    Jian Kang1, Wanshu Fan1, Yijing Li2, Rui Liu1, Dongsheng Zhou1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 607-629, 2023, DOI:10.32604/cmes.2023.024420

    Abstract With the advancement of image sensing technology, estimating 3D human pose from monocular video has become a hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequent action analysis and understanding. It empowers a wide spectrum of potential applications in various areas, such as intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methods for 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extract inter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationship extractions. In this paper, we decompose the 3D joint location regression… More >

  • Open Access


    Dual Branch PnP Based Network for Monocular 6D Pose Estimation

    Jia-Yu Liang1, Hong-Bo Zhang1,*, Qing Lei2, Ji-Xiang Du3, Tian-Liang Lin4

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3243-3256, 2023, DOI:10.32604/iasc.2023.035812

    Abstract Monocular 6D pose estimation is a functional task in the field of computer vision and robotics. In recent years, 2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based scenes. However, for monocular 6D pose estimation, these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the perspective-n-point (PnP) algorithm. There is still a difference in the distance from the expected estimation effect. To obtain a more effective feature representation result, edge enhancement is proposed to increase the shape information of the object by analyzing the influence of inaccurate 2D-3D matching on… More >

  • Open Access


    ER-Net: Efficient Recalibration Network for Multi-View Multi-Person 3D Pose Estimation

    Mi Zhou1, Rui Liu1,*, Pengfei Yi1, Dongsheng Zhou1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2093-2109, 2023, DOI:10.32604/cmes.2023.024189

    Abstract Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios. With the introduction of end-to-end direct regression methods, the field has entered a new stage of development. However, the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method. In this paper, we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy, which is applied to the multi-view multi-person 3D human pose estimation task to… More > Graphic Abstract

    ER-Net: Efficient Recalibration Network for Multi-View Multi-Person 3D Pose Estimation

  • Open Access


    Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

    K. Ishwarya, A. Alice Nithya*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6081-6099, 2023, DOI:10.32604/cmc.2023.034654

    Abstract Human pose estimation (HPE) is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision (CV) communities. HPE finds its applications in several fields namely activity recognition and human-computer interface. Despite the benefits of HPE, it is still a challenging process due to the variations in visual appearances, lighting, occlusions, dimensionality, etc. To resolve these issues, this paper presents a squirrel search optimization with a deep convolutional neural network for HPE (SSDCNN-HPE) technique. The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently.… More >

  • Open Access


    Overview of 3D Human Pose Estimation

    Jianchu Lin1,2, Shuang Li3, Hong Qin3,4, Hongchang Wang3, Ning Cui6, Qian Jiang7, Haifang Jian3,*, Gongming Wang5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1621-1651, 2023, DOI:10.32604/cmes.2022.020857

    Abstract 3D human pose estimation is a major focus area in the field of computer vision, which plays an important role in practical applications. This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos. An overall perspective of methods integrated with deep learning is introduced. Novel image-based and video-based inputs are proposed as the analysis framework. From this viewpoint, common problems are discussed. The diversity of human postures usually leads to problems such as occlusion and ambiguity, and the lack of training datasets often results in poor generalization ability of the model. Regression… More >

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