Jianchu Lin^1,2, Shuang Li³, Hong Qin^3,4, Hongchang Wang³, Ning Cui⁶, Qian Jiang⁷, Haifang Jian^3,*, Gongming Wang^5,*

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 >

S. Nandagopal^1,*, G. Karthy², A. Sheryl Oliver³, M. Subha⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1719-1733, 2023, DOI:10.32604/csse.2023.028003

Abstract Human Action Recognition (HAR) and pose estimation from videos have gained significant attention among research communities due to its application in several areas namely intelligent surveillance, human robot interaction, robot vision, etc. Though considerable improvements have been made in recent days, design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle, occlusion, background, movement speed, and so on. From the literature, it is observed that hard to deal with the temporal dimension in the action recognition process. Convolutional neural network (CNN) models could… More >

Hyun Yoo¹, Kyungyong Chung^2,*

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 1-13, 2022, DOI:10.32604/iasc.2022.024890

Abstract Human pose estimation has been a major concern in the field of computer vision. The existing method for recognizing human motion based on two-dimensional (2D) images showed a low recognition rate owing to motion depth, interference between objects, and overlapping problems. A convolutional neural network (CNN) based algorithm recently showed improved results in the field of human skeleton detection. In this study, we have combined human skeleton detection and deep neural network (DNN) to classify the motion of the human body. We used the visual geometry group network (VGGNet) CNN for human skeleton detection, and the generated skeleton coordinates were… More >

Jong-In Choi¹, Soo-Kyun Kim², Shin-Jin Kang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 747-762, 2022, DOI:10.32604/cmes.2022.018201

Abstract Two-dimensional (2D) character animation is one of the most important visual elements on which users’ interest is focused in the game field. However, 2D character animation works in the game field are mostly performed manually in two dimensions, thus generating high production costs. This study proposes a generative adversarial network based production tool that can easily and quickly generate the sprite images of 2D characters. First, we proposed a methodology to create a synthetic dataset for training using images from the real world in the game resource production field where machine learning datasets are insufficient. In addition, we have enabled… More >

Manar Elshahawy¹, Ahmed O. Aseeri^2,*, Shaker El-Sappagh^3,4, Hassan Soliman¹, Mohammed Elmogy¹, Mervat Abu-Elkheir⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 815-832, 2022, DOI:10.32604/cmc.2022.023852

Abstract The identification and classification of collective people's activities are gaining momentum as significant themes in machine learning, with many potential applications emerging. The need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd congestion. This paper investigates the capability of deep neural network (DNN) algorithms to achieve our carefully engineered pipeline for crowd analysis. It includes three principal stages that cover crowd analysis challenges. First, individual's detection is represented using the You Only Look Once (YOLO) model for human detection and Kalman filter for multiple human tracking; Second, the density… More >

Ayesha Arif¹, Yazeed Yasin Ghadi², Mohammed Alarfaj³, Ahmad Jalal¹, Shaharyar Kamal¹, Dong-Seong Kim^4,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1-18, 2022, DOI:10.32604/cmc.2022.023553

Abstract In the new era of technology, daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds. To understand the scenes and activities from human life logs, human-object interaction (HOI) is important in terms of visual relationship detection and human pose estimation. Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained. Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures, occluded regions, and unsatisfactory detection of objects, especially small-sized objects. The existing HOI detection techniques are instance-centric (object-based)… More >

Khalil Khan¹, Jehad Ali², Kashif Ahmad³, Asma Gul⁴, Ghulam Sarwar⁵, Sahib Khan⁶, Qui Thanh Hoai Ta⁷, Tae-Sun Chung⁸, Muhammad Attique^9,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1757-1770, 2021, DOI:10.32604/cmc.2020.013590

Abstract Face image analysis is one among several important cues in computer vision. Over the last five decades, methods for face analysis have received immense attention due to large scale applications in various face analysis tasks. Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation. In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model. We have developed an end to end face parts segmentation framework through deep convolutional neural networks (DCNNs). For training a deep face parts parsing model, we label face images… More >

Carlos Lopez-Franco¹, Javier Gomez-Avila², Nancy Arana-Daniel³, Alma Y. Alanis

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 431-442, 2018, DOI:10.31209/2018.100000000

Abstract This paper presents a method for 3D pose estimation using visual information and a soft-computing algorithm. The algorithm uses quaternions to represent rotations, and Particle Swarm Optimization to estimate such quaternion. The rotation estimation problem is cast as a minimization problem, which finds the best quaternion for the given data using the PSO algorithm. With this technique, the algorithm always returns a valid quaternion, and therefore a valid rotation. During the estimation process, the algorithm is able to detect and reject outliers. The simulations and experimental results show the robustness of algorithm against noise and outliers. More >

Zhengwei Li^1,2, Jianli Wang^1,*, Tao Chen¹, Bin Wang¹, Yuanhao Wu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.2, pp. 271-286, 2018, DOI:10.31614/cmc.2018.04005

Abstract With the development of adaptive optics and post restore processing techniques, large aperture ground-based telescopes can obtain high-resolution images (HRIs) of targets. The pose of the space target can be estimated from HRIs by several methods. As the target features obtained from the image are unstable, it is difficult to use existing methods for pose estimation. In this paper a method based on real-time target model matching to estimate the pose of space targets is proposed. First, the physically-constrained iterative deconvolution algorithm is used to obtain HRIs of the space target. Second, according to the 3D model, the ephemeris data,… More >