A novel technique for automated concealed face detection in surveillance videos

Mahmoud, Hanan A. Hosni; Mengash, Hanan Abdullah

doi:10.1007/s00779-020-01419-x

Cited by 31 publications

(22 citation statements)

References 32 publications

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“…Molecular analysis: drug discovery 9 (12.7) [63][64][65][66][67][68]90] Publications focused on building tools to combat the ongoing pandemic, such as building a COVID-19 imaging repository, artificial intelligence-enabled automatic cleaning and sanitizing tasks at health care facilities to assist clinical practitioners to provide timely services to the affected population.…”

Section: Publications Focused On Cementioning

confidence: 99%

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

Syeda¹,

Syed²,

Sexton³

et al. 2021

JMIR Med Inform

125

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Background SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)–based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. Objective The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. Methods A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. Results The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients’ radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. Conclusions In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

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Section: Publications Focused On Cementioning

confidence: 99%

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

Syeda¹,

Syed²,

Sexton³

et al. 2021

JMIR Med Inform

125

View full text Add to dashboard Cite

show abstract

“…Let, In(x) indicates the present intensity value at pixel x, and B(x) indicates the present background intensity value at the pixel x, then x indicates to the foreground pixel if Eq. ( 1) below is achieved [8,12].…”

Section: Hand Movement Detectionmentioning

confidence: 99%

“…The person's head orientation can be predicted using face recognition, which in turn will help in predicting the person's behavior. Face detection with movement detection is very beneficial in numerous applications such as person identification, detection of the absence or presence of persons at a particular time and place, and person verification [7][8][9]. In addition, person activities such as head motion detection, hand gesture recognition, subtle contact among two individuals, and estimation are used to innovate a system that can be identifying and recognizing abnormal behavior between students in an examination hall successfully.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Behavior Detection of Students in the Examination Hall from Surveillance Videos

Al_airaji

Aljazaery

Alrikabi

2021

Advances in Intelligent Systems and Computing

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Video analytic is a method of video processing, data collecting, and data analysis for obtaining the information of a specific domain. In the recent trend, besides analyzing each video for retrieval of the information, analyzing live surveillance video for recognizing behavior that occurs in its covering region has become more significant. This paper aims to presents a methodology that will analyze and recognize the activities of students from the videos recorded by the surveillance cameras during the exam. Automated video surveillance provides an optimal method to help monitor the students and recognize the abnormal/suspicious behavior instantly. The abnormal behavior of students may be copies of the answers from hidden sources with him such as written answers on hands, mobile-phones, summary papers, and books, or copies of answers from other students such as exchange papers between students and peeking at the others' answers papers. This work requires three main techniques: head movement detection, iris movement detection, and hand movement detection to identify the contact between the hands and face of the same student and that between different students. Automatically detect abnormal behavior will help reduce the error rate due to manual surveillance.

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“…Various object detection algorithms in deep learning (DL) have shown promising results in classifying and detecting the location of the objects [4]. The first category of DNN is a two stage approach like RCNN, faster-regional CNN (faster R-CNN), region based fully convolutional neural network (R-FCN) [5,6] etc., ISSN: 2088-8708  Real-time human detection for electricity conservation using pruned-SSD and arduino (Ushasukhanya S.)…”

Section: Introductionmentioning

confidence: 99%

Real-time human detection for electricity conservation using pruned-SSD and arduino

Ushasukhanya

Jothilakshmi

2021

IJECE

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Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.

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A novel technique for automated concealed face detection in surveillance videos

Cited by 31 publications

References 32 publications

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

Abnormal Behavior Detection of Students in the Examination Hall from Surveillance Videos

Real-time human detection for electricity conservation using pruned-SSD and arduino

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