Gyu-Il Kim¹, Hyun Yoo², Kyungyong Chung^3,*

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. Motion recognition is a challenging… More >

Mengmeng Yan¹, Chuang Zhang^1,2,*, 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 parameters; In the second step,… More >

Xuan Zhou^1,*, 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 significantly increasing the computational cost.… More >

Praveen Jesudhas^1,*, 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 of joints is represented by… More >

Tehseen Ahsan^1,*, 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 deep learning and an improved… 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 >

Chih-Ta Yen^1,*, 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 on the back of their… More >

Motasem S. Alsawadi^1,*, El-Sayed M. El-kenawy^2,3, 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 interest, it is possible to… 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 >

Sadaf Hafeez¹, Yazeed Yasin Ghadi², Mohammed Alarfaj³, Tamara al Shloul⁴, Ahmad Jalal¹, Shaharyar Kamal¹, Dong-Seong Kim^5,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4935-4952, 2022, DOI:10.32604/cmc.2022.023841

Abstract Independent human living systems require smart, intelligent, and sustainable online monitoring so that an individual can be assisted timely. Apart from ambient assisted living, the task of monitoring human activities plays an important role in different fields including virtual reality, surveillance security, and human interaction with robots. Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions. In this paper, we propose multiple methods such as random occupancy pattern, spatio temporal cloud, way-point trajectory, Hilbert transform, Walsh Hadamard transform and bone pair descriptors to extract optimal… More >