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Search Results (17)
  • Open Access


    Fuzzy Rule-Based Model to Train Videos in Video Surveillance System

    A. Manju1, A. Revathi2, M. Arivukarasi1, S. Hariharan3, V. Umarani4, Shih-Yu Chen5,*, Jin Wang6

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 905-920, 2023, DOI:10.32604/iasc.2023.038444

    Abstract With the proliferation of the internet, big data continues to grow exponentially, and video has become the largest source. Video big data introduces many technological challenges, including compression, storage, transmission, analysis, and recognition. The increase in the number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. The integration between Semantic link Networks and multimedia resources provides a new prospect for organizing them with their semantics. The tags and surrounding texts of multimedia resources are used to measure their semantic association. Two evaluation methods including clustering and… More >

  • Open Access


    Automated Video-Based Face Detection Using Harris Hawks Optimization with Deep Learning

    Latifah Almuqren1, Manar Ahmed Hamza2,*, Abdullah Mohamed3, Amgad Atta Abdelmageed2

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4917-4933, 2023, DOI:10.32604/cmc.2023.037738

    Abstract Face recognition technology automatically identifies an individual from image or video sources. The detection process can be done by attaining facial characteristics from the image of a subject face. Recent developments in deep learning (DL) and computer vision (CV) techniques enable the design of automated face recognition and tracking methods. This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking (HHODL-AFDT) method. The proposed HHODL-AFDT model involves a Faster region based convolution neural network (RCNN)-based face detection model and HHO-based hyperparameter optimization process. The presented optimal Faster RCNN model… More >

  • Open Access


    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

    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


    An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

    Sareer Ul Amin1, Yongjun Kim2, Irfan Sami3, Sangoh Park1,*, Sanghyun Seo4,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3939-3958, 2023, DOI:10.32604/csse.2023.034805

    Abstract In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance… More >

  • Open Access


    Quantum Computing Based Neural Networks for Anomaly Classification in Real-Time Surveillance Videos

    MD. Yasar Arafath1,*, A. Niranjil Kumar2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2489-2508, 2023, DOI:10.32604/csse.2023.035732

    Abstract For intelligent surveillance videos, anomaly detection is extremely important. Deep learning algorithms have been popular for evaluating real-time surveillance recordings, like traffic accidents, and criminal or unlawful incidents such as suicide attempts. Nevertheless, Deep learning methods for classification, like convolutional neural networks, necessitate a lot of computing power. Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics. As a result, the focus of this research is on developing a hybrid quantum computing model which is based on deep learning. This research develops a Quantum Computing-based Convolutional Neural Network… More >

  • Open Access


    Realtime Object Detection Through M-ResNet in Video Surveillance System

    S. Prabu1,*, J. M. Gnanasekar2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2257-2271, 2023, DOI:10.32604/iasc.2023.029877

    Abstract Object detection plays a vital role in the video surveillance systems. To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks. However, monitoring the video continually at a quicker pace is a challenging job. As a consequence, security cameras are useless and need human monitoring. The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions. The anomalous action does not occur at a higher rate than usual occurrences. To detect the object in a video,… More >

  • Open Access


    Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

    Erssa Arif1,*, Syed Khuram Shahzad2, Muhammad Waseem Iqbal3, Muhammad Arfan Jaffar4, Abdullah S. Alshahrani5, Ahmed Alghamdi6

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4615-4630, 2022, DOI:10.32604/cmc.2022.027571

    Abstract The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution More >

  • Open Access


    Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

    Esam A. AlQaralleh1, Fahad Aldhaban2, Halah Nasseif2, Malek Z. Alksasbeh3, Bassam A. Y. Alqaralleh2,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5593-5605, 2022, DOI:10.32604/cmc.2022.026666

    Abstract Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes. The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification. The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention. Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level. The advancements of the DL models help to accomplish improved… More >

  • Open Access


    A Template Matching Based Feature Extraction for Activity Recognition

    Muhammad Hameed Siddiqi1,*, Helal Alshammari1, Amjad Ali2, Madallah Alruwaili1, Yousef Alhwaiti1, Saad Alanazi1, M. M. Kamruzzaman1

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 611-634, 2022, DOI:10.32604/cmc.2022.024760

    Abstract Human activity recognition (HAR) can play a vital role in the monitoring of human activities, particularly for healthcare conscious individuals. The accuracy of HAR systems is completely reliant on the extraction of prominent features. Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities, thereby reducing recognition performance. In this paper, we propose a robust feature extraction method for HAR systems based on template matching. Essentially, in this method, we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette. In this regard,… More >

  • Open Access


    Abnormality Identification in Video Surveillance System using DCT

    A. Balasundaram1,*, Golda Dilip2, M. Manickam3, Arun Kumar Sivaraman4, K. Gurunathan5, R. Dhanalakshmi6, S. Ashokkumar7

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 693-704, 2022, DOI:10.32604/iasc.2022.022241

    Abstract In the present world, video surveillance methods play a vital role in observing the activities that take place across secured and unsecured environment. The main aim with which a surveillance system is deployed is to spot abnormalities in specific areas like airport, military, forests and other remote areas, etc. A new block-based strategy is represented in this paper. This strategy is used to identify unusual circumstances by examining the pixel-wise frame movement instead of the standard object-based approaches. The density and also the speed of the movement is extorted by utilizing optical flow. The proposed More >

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