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  • Open Access

    ARTICLE

    Suspicious Activities Recognition in Video Sequences Using DarkNet-NasNet Optimal Deep Features

    Safdar Khan1, Muhammad Attique Khan2, Jamal Hussain Shah1,*, Faheem Shehzad2, Taerang Kim3, Jae-Hyuk Cha3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2337-2360, 2023, DOI:10.32604/csse.2023.040410

    Abstract Human Suspicious Activity Recognition (HSAR) is a critical and active research area in computer vision that relies on artificial intelligence reasoning. Significant advances have been made in this field recently due to important applications such as video surveillance. In video surveillance, humans are monitored through video cameras when doing suspicious activities such as kidnapping, fighting, snatching, and a few more. Although numerous techniques have been introduced in the literature for routine human actions (HAR), very few studies are available for HSAR. This study proposes a deep convolutional neural network (CNN) and optimal featuresbased framework for HSAR in video frames. The… More >

  • Open Access

    ARTICLE

    A Triplet-Branch Convolutional Neural Network for Part-Based Gait Recognition

    Sang-Soo Yeo1, Seungmin Rho2,*, Hyungjoon Kim3, Jibran Safdar4, Umar Zia5, Mehr Yahya Durrani5

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2027-2047, 2023, DOI:10.32604/csse.2023.040327

    Abstract Intelligent vision-based surveillance systems are designed to deal with the gigantic volume of videos captured in a particular environment to perform the interpretation of scenes in form of detection, tracking, monitoring, behavioral analysis, and retrievals. In addition to that, another evolving way of surveillance systems in a particular environment is human gait-based surveillance. In the existing research, several methodological frameworks are designed to use deep learning and traditional methods, nevertheless, the accuracies of these methods drop substantially when they are subjected to covariate conditions. These covariate variables disrupt the gait features and hence the recognition of subjects becomes difficult. To… More >

  • Open Access

    ARTICLE

    Anomalous Situations Recognition in Surveillance Images Using Deep Learning

    Qurat-ul-Ain Arshad1, Mudassar Raza1, Wazir Zada Khan2, Ayesha Siddiqa2, Abdul Muiz2, Muhammad Attique Khan3,*, Usman Tariq4, Taerang Kim5, Jae-Hyuk Cha5,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1103-1125, 2023, DOI:10.32604/cmc.2023.039752

    Abstract Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the detection accuracy of various anomalous… More >

  • Open Access

    ARTICLE

    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 retrieval are performed to measure… More >

  • Open Access

    ARTICLE

    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 precisely recognizes the face and… More >

  • 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

    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 transfer learning in the training… More >

  • Open Access

    ARTICLE

    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 video (ADSV). At the input… More >

  • Open Access

    ARTICLE

    Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

    Ali Raza1, Samia Allaoua Chelloug2,*, Mohammed Hamad Alatiyyah3, Ahmad Jalal1, Jeongmin Park4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3275-3289, 2023, DOI:10.32604/cmc.2023.029719

    Abstract Pedestrian detection and tracking are vital elements of today’s surveillance systems, which make daily life safe for humans. Thus, human detection and visualization have become essential inventions in the field of computer vision. Hence, developing a surveillance system with multiple object recognition and tracking, especially in low light and night-time, is still challenging. Therefore, we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night. In particular, we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared (IR) images using machine… More >

  • Open Access

    ARTICLE

    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 (QC-CNN) to extract features and… More >

  • Open Access

    ARTICLE

    Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

    Naqqash Dilshad1, Taimoor Khan2, JaeSeung Song1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 749-764, 2023, DOI:10.32604/csse.2023.034475

    Abstract To prevent economic, social, and ecological damage, fire detection and management at an early stage are significant yet challenging. Although computationally complex networks have been developed, attention has been largely focused on improving accuracy, rather than focusing on real-time fire detection. Hence, in this study, the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment. The proposed model architecture is inspired by the VGG16 network, with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4. This results in higher performance with a reduced number of… More >

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