Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (4)
  • Open Access

    ARTICLE

    ISHD: Intelligent Standing Human Detection of Video Surveillance for the Smart Examination Environment

    Wu Song1, Yayuan Tang2,3,*, Wenxue Tan1, Sheng Ren1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 509-526, 2023, DOI:10.32604/cmes.2023.026933 - 23 April 2023

    Abstract In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior (human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligent standing human detection (ISHD) method based on an improved single shot multibox detector to detect the target of standing human posture in the scene frame of exam room video surveillance at a specific examination stage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posture feature extractor of a standing person, merges prior knowledge, and introduces More >

  • Open Access

    ARTICLE

    Human Verification over Activity Analysis via Deep Data Mining

    Kumar Abhishek1,*, Sheikh Badar ud din Tahir2

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1391-1409, 2023, DOI:10.32604/cmc.2023.035894 - 06 February 2023

    Abstract Human verification and activity analysis (HVAA) are primarily employed to observe, track, and monitor human motion patterns using red-green-blue (RGB) images and videos. Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times. Numerous models rely on various parameters, such as the detection rate, position, and direction of human body components in RGB images. This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features. To use human interaction image sequences as input data, we first perform a few denoising steps.… More >

  • Open Access

    ARTICLE

    Automatic Human Detection Using Reinforced Faster-RCNN for Electricity Conservation System

    S. Ushasukhanya*, M. Karthikeyan

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1261-1275, 2022, DOI:10.32604/iasc.2022.022654 - 17 November 2021

    Abstract Electricity conservation systems are designed to conserve electricity to manage the bridge between the high raising demand and the production. Such systems have been so far using sensors to detect the necessity which adds an additional cost to the setup. Closed-circuit Television (CCTV) has been installed in almost everywhere around us especially in commercial places. Interpretation of these CCTV images is being carried out for various reasons to elicit the information from it. Hence a framework for electricity conservation that enables the electricity supply only when required, using existing resources would be a cost effective… More >

  • Open Access

    ARTICLE

    Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

    Syed Muhammad Saqlain Shah1,*, Tahir Afzal Malik2, Robina khatoon1, Syed Saqlain Hassan3, Faiz Ali Shah4

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 535-553, 2019, DOI:10.32604/cmc.2019.07948

    Abstract Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed More >

Displaying 1-10 on page 1 of 4. Per Page