Jian Kang¹, Wanshu Fan¹, Yijing Li², Rui Liu¹, Dongsheng Zhou^1,*

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 >

Faheem Shehzad¹, Muhammad Attique Khan², Muhammad Asfand E. Yar³, Muhammad Sharif¹, Majed Alhaisoni⁴, Usman Tariq⁵, Arnab Majumdar⁶, Orawit Thinnukool^7,*

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, a deep learning and improved… More >

Yang Wu¹, Lei Guo¹, Xiaodong Dai¹, Bin Zhang¹, Dong-Won Park², Ming Ma^1,*

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 this paper, instead of the… More >

Jiahui Pan^1,2,*, Liangxin Liu¹, Mianfen Lin¹, Shengzhou Luo¹, Chengju Zhou¹, Huijian Liao³, Fei Wang^1,2

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 673-688, 2021, DOI:10.32604/iasc.2021.020240

Abstract Abnormal behavior detection is an essential step in a wide range of application domains, such as smart video surveillance. In this study, we proposed an improved two-stream inflated 3D ConvNet network approach based on probability regression for abnormal behavior detection. The proposed approach consists of four parts: (1) preprocessing pretreatment for the input video; (2) dynamic feature extraction from video streams using a two-stream inflated 3D (I3D) ConvNet network; (3) visual feature transfer into a two-dimensional matrix; and (4) feature classification using a generalized regression neural network (GRNN), which ultimately achieves a probability regression. Compared with the traditional methods, two-stream… More >

Weibo Yang¹, Weidong Liu^{2, *}, Jinming Liu³, Mingyang Zhang⁴

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 515-525, 2020, DOI:10.32604/cmc.2020.09801

Abstract The Convolutional Neural Network (CNN) is a widely used deep neural network. Compared with the shallow neural network, the CNN network has better performance and faster computing in some image recognition tasks. It can effectively avoid the problem that network training falls into local extremes. At present, CNN has been applied in many different fields, including fault diagnosis, and it has improved the level and efficiency of fault diagnosis. In this paper, a two-streams convolutional neural network (TCNN) model is proposed. Based on the short-time Fourier transform (STFT) spectral and Mel Frequency Cepstrum Coefficient (MFCC) input characteristics of two-streams acoustic… More >

Shuren Zhou^{1, *}, Le Chen¹, Vijayan Sugumaran²

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1545-1561, 2020, DOI:10.32604/cmc.2020.09867

Abstract The two-stream convolutional neural network exhibits excellent performance in the video action recognition. The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extracted by the frames, to train a model each, and to finally integrate the outputs of the two models. Nevertheless, the reliance on the pre-extraction of the optical flow impedes the efficiency of action recognition, and the temporal and the spatial streams are just simply fused at the ends, with one stream failing and the other stream succeeding. We propose a novel hidden twostream collaborative (HTSC) learning… More >