An Automated Surveillance System for Public Places

2014 ◽  
pp. 197-226
Author(s):  
Kumar Ray ◽  
Debayan Ganguly ◽  
Kingshuk Chatterjee
Keyword(s):  
Surveillance System ◽  
Public Places ◽  
Automated Surveillance

scholarly journals 911Development of an Automated Surveillance System for Identifying Surgical Site Infections and Triaging Clinical Review

2014 ◽  
Vol 1 (suppl_1) ◽  
pp. S263-S263
Author(s):  
Westyn Branch-Elliman ◽  
Judith Strymish ◽  
Kamal Itani ◽  
Kalpana Gupta
Keyword(s):  
Clinical Review ◽  
Surveillance System ◽  
Surgical Site Infections ◽  
Automated Surveillance

scholarly journals 889Assessment of an Automated Surveillance System for Detection of Ventilator Associated Events

2014 ◽  
Vol 1 (suppl_1) ◽  
pp. S255-S256
Author(s):  
Dooshanveer Nuckchady ◽  
Michael G. Heckman ◽  
Tara Creech ◽  
Darlene Carey ◽  
Robert Domnick ◽  
...  
Keyword(s):  
Surveillance System ◽  
Automated Surveillance

Design considerations for the processing system of a CNN-based automated surveillance system

2019 ◽  
Vol 136 ◽  
pp. 105-114 ◽  
Author(s):  
James A.D. Cameron ◽  
Patrick Savoie ◽  
Mary E. Kaye ◽  
Erik J. Scheme
Keyword(s):  
Surveillance System ◽  
Processing System ◽  
Design Considerations ◽  
Automated Surveillance

Human Detection Framework for Automated Surveillance Systems

2016 ◽  
Vol 6 (2) ◽  
pp. 877
Author(s):  
Redwan A.K. Noaman ◽  
Mohd Alauddin Mohd Ali ◽  
Nasharuddin Zainal ◽  
Faisal Saeed
Keyword(s):  
Object Recognition ◽  
Surveillance System ◽  
Human Detection ◽  
Surveillance Systems ◽  
Environment Modeling ◽  
Research Attention ◽  
Area Of Interest ◽  
Edge Detecting ◽  
Object Edge ◽  
Automated Surveillance

Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not suitable for surveillance systems for real-time monitoring. In this paper, a holistic framework is proposed for enhancing the performance of human detection in surveillance system. In general, the framework includes the following stages: environment modeling, motion object detection, and human object recognition. In environment modeling, modal algorithm has been suggested for background initialization and extraction. Then for effectively classifying the motion object, edge detecting and B-spline algorithm have been used for shadow detection and removal. Then, enhanced Lucas–Kanade optical flow has been used to get the area of interest for object segmentation. Finally, to enhance the segmentation, some morphological processes were performed. In the motion object recognition stage, segmentation for each blob is performed and processed to the human detector which is a complete learning-based system for detecting and localizing objects/humans in images using mixtures of deformable part models (PFF detector). Results show enhancement in each phase of the proposed framework. These enhancements are shown in the overall performance of human detection in surveillance system.

scholarly journals Development and Evaluation of a Fully Automated Surveillance System for Influenza-Associated Hospitalization at a Multihospital Health System in Northeast Ohio

2020 ◽  
Vol 11 (04) ◽  
pp. 564-569
Author(s):  
Patrick C. Burke ◽  
Rachel Benish Shirley ◽  
Jacob Raciniewski ◽  
James F. Simon ◽  
Robert Wyllie ◽  
...  
Keyword(s):  
Health System ◽  
Surveillance System ◽  
Cost Savings ◽  
Automated System ◽  
Surveillance Systems ◽  
Predictive Values ◽  
Visualization Software ◽  
Independent Information ◽  
Influenza Activity ◽  
Automated Surveillance

Abstract Background Performing high-quality surveillance for influenza-associated hospitalization (IAH) is challenging, time-consuming, and essential. Objectives Our objectives were to develop a fully automated surveillance system for laboratory-confirmed IAH at our multihospital health system, to evaluate the performance of the automated system during the 2018 to 2019 influenza season at eight hospitals by comparing its sensitivity and positive predictive value to that of manual surveillance, and to estimate the time and cost savings associated with reliance on the automated surveillance system. Methods Infection preventionists (IPs) perform manual surveillance for IAH by reviewing laboratory records and making a determination about each result. For automated surveillance, we programmed a query against our Enterprise Data Vault (EDV) for cases of IAH. The EDV query was established as a dynamic data source to feed our data visualization software, automatically updating every 24 hours.To establish a gold standard of cases of IAH against which to evaluate the performance of manual and automated surveillance systems, we generated a master list of possible IAH by querying four independent information systems. We reviewed medical records and adjudicated whether each possible case represented a true case of IAH. Results We found 844 true cases of IAH, 577 (68.4%) of which were detected by the manual system and 774 (91.7%) of which were detected by the automated system. The positive predictive values of the manual and automated systems were 89.3 and 88.3%, respectively.Relying on the automated surveillance system for IAH resulted in an average recoup of 82 minutes per day for each IP and an estimated system-wide payroll redirection of $32,880 over the four heaviest weeks of influenza activity. Conclusion Surveillance for IAH can be entirely automated at multihospital health systems, saving time, and money while improving case detection.

scholarly journals RANCANG BANGUN APLIKASI NEW NORMAL COVID-19 DETEKSI PENGGUNAAN MASKER MENGGUNAKAN HAAR CASCADE CLASSIFIER

2021 ◽  
Vol 15 (2) ◽  
pp. 199-209
Author(s):  
Ade chandra Saputra ◽  
Ahmadi Ahmadi ◽  
Ariesta Lestari
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Programming Language ◽  
Surveillance System ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Public Places ◽  
The Face ◽  
Haar Cascade ◽  
Analysis Design

During the COVID-19 pandemic, when in public places, it is required to apply the 4M health protocol, namely wearing masks, washing hands, maintaining distance, and avoiding crowds. In its implementation, there are officers who always maintain and remind people not to violate health protocols. Like remembering to wear a mask. The mask detection application is made as a computerized surveillance system that can store images of violations of the use of masks and provide warning sounds. Observations, discussions and literature studies are sources of data in this empirical research. Using Python as a programming language assisted with OpenCV for image processing. After passing through the 4 stages of Waterfall, namely Analysis, Design, Manufacturing and Development and Testing, an application is produced where the Raspberry Pi is a processing tool and images are captured from the camera module with a resolution of 1080x1024 px. This application can detect the use of masks with an accuracy of 90.5% using the Machine Learning Haar Cascade Classifier method. Where the condition of the face is a maximum of 30 degrees turned to the side and looked up

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