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  • Open Access


    Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

    Dmitry Gura1,2, Bo Dong3,*, Duaa Mehiar4, Nidal Al Said5

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1995-2014, 2024, DOI:10.32604/cmc.2024.048238

    Abstract The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake… More >

  • Open Access


    Deep Learning-Based Action Classification Using One-Shot Object Detection

    Hyun Yoo1, Seo-El Lee2, Kyungyong Chung3,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1343-1359, 2023, DOI:10.32604/cmc.2023.039263

    Abstract Deep learning-based action classification technology has been applied to various fields, such as social safety, medical services, and sports. Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions. There are various related studies on the real-time classification of actions in an image. However, existing deep learning-based action classification models have prolonged response speeds, so there is a limit to real-time analysis. In addition, it has low accuracy of action of each object if multiple objects appear in the image. Also, it needs… More >

  • Open Access


    SlowFast Based Real-Time Human Motion Recognition with Action Localization

    Gyu-Il Kim1, Hyun Yoo2, Kyungyong Chung3,*

    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.… More >

  • Open Access


    Automatic Team Assignment and Jersey Number Recognition in Football Videos

    Ragd Alhejaily1, Rahaf Alhejaily1, Mai Almdahrsh1, Shareefah Alessa1, Saleh Albelwi1,2,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2669-2684, 2023, DOI:10.32604/iasc.2023.033062

    Abstract Football is one of the most-watched sports, but analyzing players’ performance is currently difficult and labor intensive. Performance analysis is done manually, which means that someone must watch video recordings and then log each player’s performance. This includes the number of passes and shots taken by each player, the location of the action, and whether or not the play had a successful outcome. Due to the time-consuming nature of manual analyses, interest in automatic analysis tools is high despite the many interdependent phases involved, such as pitch segmentation, player and ball detection, assigning players to… More >

  • Open Access


    An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3

    Mubashir Javaid1, Muazzam Maqsood2, Farhan Aadil2, Jibran Safdar1, Yongsung Kim3,*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1295-1310, 2023, DOI:10.32604/iasc.2023.028262

    Abstract Currently, worldwide industries and communities are concerned with building, expanding, and exploring the assets and resources found in the oceans and seas. More precisely, to analyze a stock, archaeology, and surveillance, several cameras are installed underseas to collect videos. However, on the other hand, these large size videos require a lot of time and memory for their processing to extract relevant information. Hence, to automate this manual procedure of video assessment, an accurate and efficient automated system is a greater necessity. From this perspective, we intend to present a complete framework solution for the task… More >

  • Open Access


    Classification of Multi-Frame Human Motion Using CNN-based Skeleton Extraction

    Hyun Yoo1, Kyungyong Chung2,*

    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 More >

  • Open Access


    Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

    Durr-e-Nayab1, Ali Mustafa Qamar2,*, Rehan Ullah Khan3, Waleed Albattah3, Khalil Khan4, Shabana Habib3, Muhammad Islam5

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5581-5601, 2022, DOI:10.32604/cmc.2022.022153

    Abstract The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah… More >

  • Open Access


    Study on the Effect of Shoulder Training on the Mechanics of Tennis Serve Speed through Video Analysis

    Wei Jiang1,*, Gang He2

    Molecular & Cellular Biomechanics, Vol.18, No.4, pp. 221-229, 2021, DOI:10.32604/mcb.2021.017050

    Abstract Tennis service is an important part of winning a match. This study analyzed the mechanics of tennis serving speed and divided ten tennis players into two groups. One group carried out conventional training, while the other group carried out auxiliary training on shoulders through elastic band besides conventional training. The actions were photographed by cameras and analyzed. The results showed that the throwing height and hitting point height of the two groups improved after the experiment, and p < 0.05 in the comparison between Groups A and B; the ball deflection angle reduced after throwing, but the improvement More >

  • Open Access


    PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance

    Juyoung Park1,*, Jung Hee Lee1, Junseong Bang2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 965-976, 2021, DOI:10.32604/cmes.2021.014669


    We propose a mobile system, called PotholeEye+, for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye+ pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. Every day for a year, we have tested PotholeEye+ on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye+ detected the pavement distress with accuracy of 92%, precision of 87% and recall 74% averagely during driving at an average speed of

    More >

  • Open Access


    Methodology for Detecting Strabismus through Video Analysis and Intelligent Mining Techniques

    Hanan Abdullah Mengash1,*, Hanan A. Hosni Mahmoud1,2

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1013-1032, 2021, DOI:10.32604/cmc.2021.014942

    Abstract Strabismus is a medical condition that is defined as the lack of coordination between the eyes. When Strabismus is detected at an early age, the chances of curing it are higher. The methods used to detect strabismus and measure its degree of deviation are complex and time-consuming, and they always require the presence of a physician. In this paper, we present a method of detecting strabismus and measuring its degree of deviation using videos of the patient’s eye region under a cover test. Our method involves extracting features from a set of training videos (training… More >

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