Chan Hyeong Cho, Hyun Min Song^*, Taek-Young Youn^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1911-1931, 2024, DOI:10.32604/cmes.2024.053430 - 31 October 2024

Abstract With the advancement of video recording devices and network infrastructure, we use surveillance cameras to protect our valuable assets. This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security. The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest (ROIs) within the video, linking these ROIs to generate unique IDs. These IDs are then combined with a master key to create entity-specific keys, which are used to encrypt the ROIs within the video. This system supports selective decryption, effectively protecting personal information More >

Vesal Khean¹, Chomyong Kim², Sunjoo Ryu², Awais Khan¹, Min Kyung Hong³, Eun Young Kim⁴, Joungmin Kim⁵, Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 773-787, 2024, DOI:10.32604/cmc.2024.056767 - 15 October 2024

Abstract Human Interaction Recognition (HIR) was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements. HIR requires more sophisticated analysis than Human Action Recognition (HAR) since HAR focuses solely on individual activities like walking or running, while HIR involves the interactions between people. This research aims to develop a robust system for recognizing five common human interactions, such as hugging, kicking, pushing, pointing, and no interaction, from video sequences using multiple cameras. In this study, a hybrid Deep… More >

Sareer Ul Amin¹, Yongjun Kim², Irfan Sami³, Sangoh Park^1,*, Sanghyun Seo^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3939-3958, 2023, DOI:10.32604/csse.2023.034805 - 03 April 2023

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 >

MD. Yasar Arafath^1,*, A. Niranjil Kumar²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2489-2508, 2023, DOI:10.32604/csse.2023.035732 - 09 February 2023

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 >

S. Priyadharshini^*, Ansuman Mahapatra

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2603-2616, 2023, DOI:10.32604/csse.2023.032506 - 21 December 2022

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 More >

L. Amudha^1,*, R. PushpaLakshmi²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2299-2312, 2023, DOI:10.32604/csse.2023.031169 - 21 December 2022

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… More >

R. Haripriya^*, G. Kousalya

Computer Systems Science and Engineering, Vol.43, No.3, pp. 947-965, 2022, DOI:10.32604/csse.2022.024149 - 09 May 2022

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… More >

Zhang Yunzuo^1,*, Zhang Jiayu¹, Cai Zhaoquan²

Journal of New Media, Vol.4, No.1, pp. 1-11, 2022, DOI:10.32604/jnm.2022.027054 - 21 April 2022

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… More >

M. Murugesan^*, S. Thilagamani

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 389-405, 2022, DOI:10.32604/iasc.2022.024641 - 15 April 2022

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… More >

Zhen Xu¹, Xiaoqian Zeng¹, Genlin Ji^1,*, Bo Sheng²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1703-1717, 2022, DOI:10.32604/iasc.2022.016919 - 09 October 2021

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… More >