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  • 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 video (ADSV). At the input… 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 (QC-CNN) to extract features and… More >

  • Open Access


    A Personalized Video Synopsis Framework for Spherical Surveillance Video

    S. Priyadharshini*, Ansuman Mahapatra

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2603-2616, 2023, DOI:10.32604/csse.2023.032506

    Abstract Video synopsis is an effective way to easily summarize long-recorded surveillance videos. The omnidirectional view allows the observer to select the desired fields of view (FoV) from the different FoV available for spherical surveillance video. By choosing to watch one portion, the observer misses out on the events occurring somewhere else in the spherical scene. This causes the observer to experience fear of missing out (FOMO). Hence, a novel personalized video synopsis approach for the generation of non-spherical videos has been introduced to address this issue. It also includes an action recognition module that makes it easy to display necessary… More >

  • Open Access


    Performance Analysis of Hybrid RR Algorithm for Anomaly Detection in Streaming Data

    L. Amudha1,*, R. PushpaLakshmi2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2299-2312, 2023, DOI:10.32604/csse.2023.031169

    Abstract Automated live video stream analytics has been extensively researched in recent times. Most of the traditional methods for video anomaly detection is supervised and use a single classifier to identify an anomaly in a frame. We propose a 3-stage ensemble-based unsupervised deep reinforcement algorithm with an underlying Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). In the first stage, an ensemble of LSTM-RNNs are deployed to generate the anomaly score. The second stage uses the least square method for optimal anomaly score generation. The third stage adopts award-based reinforcement learning to update the model. The proposed Hybrid Ensemble… More >

  • Open Access


    Primary Contacts Identification for COVID-19 Carriers from Surveillance Videos

    R. Haripriya*, G. Kousalya

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 947-965, 2022, DOI:10.32604/csse.2022.024149

    Abstract COVID-19 (Coronavirus disease of 2019) is caused by SARS-CoV2 (Severe Acute Respiratory Syndrome Coronavirus 2) and it was first diagnosed in December 2019 in China. As of 25th Aug 2021, there are 165 million confirmed COVID-19 positive cases and 4.4 million deaths globally. As of today, though there are approved COVID-19 vaccine candidates only 4 billion doses have been administered. Until 100% of the population is safe, no one is safe. Even though these vaccines can provide protection against getting seriously ill and dying from the disease, it does not provide 100% protection from getting infected and passing it on… More >

  • Open Access


    Key Frame Extraction Algorithm of Surveillance Video Based on Quaternion Fourier Significance Detection

    Zhang Yunzuo1,*, Zhang Jiayu1, Cai Zhaoquan2

    Journal of New Media, Vol.4, No.1, pp. 1-11, 2022, DOI:10.32604/jnm.2022.027054

    Abstract With the improvement of people's security awareness, numerous monitoring equipment has been put into use, resulting in the explosive growth of surveillance video data. Key frame extraction technology is a paramount technology for improving video storage efficiency and enhancing the accuracy of video retrieval. It can extract key frame sets that can express video content from massive videos. However, the existing key frame extraction algorithms of surveillance video still have deficiencies, such as the destruction of image information integrity and the inability to extract key frames accurately. To this end, this paper proposes a key frame extraction algorithm of surveillance… More >

  • Open Access


    Bayesian Feed Forward Neural Network-Based Efficient Anomaly Detection from Surveillance Videos

    M. Murugesan*, S. Thilagamani

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 389-405, 2022, DOI:10.32604/iasc.2022.024641

    Abstract Automatic anomaly activity detection is difficult in video surveillance applications due to variations in size, type, shape, and objects’ location. The traditional anomaly detection and classification methods may affect the overall segmentation accuracy. It requires the working groups to judge their constant attention if the captured activities are anomalous or suspicious. Therefore, this defect creates the need to automate this process with high accuracy. In addition to being extraordinary or questionable, the display does not contain the necessary recording frame and activity standard to help the quick judgment of the parts’ specialized action. Therefore, to reduce the wastage of time… More >

  • Open Access


    Improved Anomaly Detection in Surveillance Videos with Multiple Probabilistic Models Inference

    Zhen Xu1, Xiaoqian Zeng1, Genlin Ji1,*, Bo Sheng2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1703-1717, 2022, DOI:10.32604/iasc.2022.016919

    Abstract Anomaly detection in surveillance videos is an extremely challenging task due to the ambiguous definitions for abnormality. In a complex surveillance scenario, the kinds of abnormal events are numerous and might co-exist, including such as appearance and motion anomaly of objects, long-term abnormal activities, etc. Traditional video anomaly detection methods cannot detect all these kinds of abnormal events. Hence, we utilize multiple probabilistic models inference to detect as many different kinds of abnormal events as possible. To depict realistic events in a scene, the parameters of our methods are tailored to the characteristics of video sequences of practical surveillance scenarios.… More >

  • Open Access


    Key Frame Extraction of Surveillance Video Based on Frequency Domain Analysis

    Yunzuo Zhang1,*, Shasha Zhang1, Jiayu Zhang1, Kaina Guo1, Zhaoquan Cai2

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 259-272, 2021, DOI:10.32604/iasc.2021.017200

    Abstract Video key frame extraction, reputed as an essential step in video analysis and content-based video retrieval, and meanwhile, also serves as the basis and premise of generating video synopsis. Video key frame extraction means extracting the meaningful parts of the video by analyzing their content and structure to form a concise and semantically expressive summary. Up to now, people have achieved many research results in key frame extraction. Nevertheless, because the surveillance video has no specific structure, such as news, sports games, and other videos, it is not accurate enough to directly extract the key frame with the existing effective… More >

  • Open Access


    Surveillance Video Key Frame Extraction Based on Center Offset

    Yunzuo Zhang1,*, Shasha Zhang1, Yi Li1, Jiayu Zhang1, Zhaoquan Cai2, Shui Lam3

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4175-4190, 2021, DOI:10.32604/cmc.2021.017011

    Abstract With the explosive growth of surveillance video data, browsing videos quickly and effectively has become an urgent problem. Video key frame extraction has received widespread attention as an effective solution. However, accurately capturing the local motion state changes of moving objects in the video is still challenging in key frame extraction. The target center offset can reflect the change of its motion state. This observation proposed a novel key frame extraction method based on moving objects center offset in this paper. The proposed method utilizes the center offset to obtain the global and local motion state information of moving objects,… More >

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