scholarly journals Real-Time Pedestrian Tracking and Counting with TLD

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Jiawei Shi ◽  
Xianmei Wang ◽  
Huer Xiao
Keyword(s):  
Real Time ◽  
Similarity Measure ◽  
Buffer Zone ◽  
Background Modeling ◽  
Single Line ◽  
Pedestrian Tracking ◽  
Public Database ◽  
The Public ◽  
Modeling Algorithm

This paper describes a solution to solve the issue of automatic multipedestrian tracking and counting. First, background modeling algorithm is applied to actively obtain multipedestrian candidates, followed by a confirmation step with classification. Then each pedestrian patch is handled by real-time TLD (Tracking-Learning-Detection) to get a new predication position according to similarity measure. Further TLD results are compared with classification list to determine a new, disappeared, or existing pedestrian. Finally single line counting with buffer zone is employed to count pedestrians. Experiments results on the public database, PETS, demonstrate the validity of our solution.

scholarly journals Correlation Between SARS-Cov-2 Vaccination, COVID-19 Incidence and Mortality: Tracking the Effect of Vaccination on Population Protection in Real Time

2021 ◽  
Vol 12 ◽  
Author(s):  
Kiyoshi F. Fukutani ◽  
Mauricio L. Barreto ◽  
Bruno B. Andrade ◽  
Artur T. L. Queiroz
Keyword(s):  
Real Time ◽  
Social Distance ◽  
Rapid Development ◽  
Face Mask ◽  
Public Database ◽  
The Public ◽  
Start Date ◽  
Incidence And Mortality ◽  
The World

Coronavirus disease 19 (COVID-19) has struck the world since the ending of 2019. Tools for pandemic control were scarce, limited only to social distance and face mask usage. Today, upto 12 vaccines were approved and the rapid development raises questions about the vaccine efficiency. We accessed the public database provided by each country and the number of death, active cases, and tests in order to evaluate how the vaccine is influencing the COVID-19 pandemic. We observed distinct profiles across the countries and it was related to the vaccination start date and we are proposing a new way to manage the vaccination.

A Statistical Background Modeling Algorithm for Real-Time Pixel Classification

2018 ◽  
Vol 22 (3) ◽  
Author(s):  
Ricardo Acevedo-Avila ◽  
Miguel Gonzalez-Mendoza ◽  
Andres Garcia-Garcia
Keyword(s):  
Real Time ◽  
Background Modeling ◽  
Pixel Classification ◽  
Statistical Background ◽  
Modeling Algorithm

scholarly journals Daily Human Activity Recognition Using Non-Intrusive Sensors

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5270
Author(s):  
Raúl Gómez Ramos ◽  
Jaime Duque Domingo ◽  
Eduardo Zalama ◽  
Jaime Gómez-García-Bermejo
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Elderly Person ◽  
The Elderly ◽  
Public Database ◽  
Accuracy Rate ◽  
The Public ◽  
The Neural Network ◽  
Reliable Model

In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

scholarly journals The case for tracking misinformation the way we track disease

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110138
Author(s):  
Erika Bonnevie ◽  
Jennifer Sittig ◽  
Joe Smyser
Keyword(s):  
Public Health ◽  
Real Time ◽  
Supervised Machine Learning ◽  
Disease Spread ◽  
The Public ◽  
Monitoring Tools ◽  
Core Function ◽  
Public Health Organizations ◽  
Front Line Workers ◽  
Media Data

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.

Real time computer communications and the public interest

1968 ◽  
Author(s):  
Michael M. Gold ◽  
Lee L. Selwyn
Keyword(s):  
Real Time ◽  
Public Interest ◽  
The Public ◽  
Computer Communications

scholarly journals The Necessity of Mobile Phone Technologies for Public Health Surveillance in Benin

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yaovi M. G. Hounmanou ◽  
Murielle S. S. Agonsanou ◽  
Victorien Dougnon ◽  
Mahougnon H. B. Vodougnon ◽  
Ephraim M. Achoh ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Smart Phone ◽  
Cross Sectional Study ◽  
Health Surveillance ◽  
Medical Professionals ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
The Public ◽  
Phone Calls

A cross-sectional study was conducted in March 2016 to assess the need of mobile phone technologies for health surveillance and interventions in Benin. Questionnaires were administered to 130 individuals comprising 25 medical professionals, 33 veterinarians, and 72 respondents from the public. All respondents possess cell phones and 75%, 84%, and 100% of the public, medical professionals, and veterinarians, respectively, generally use them for medical purposes. 75% of respondents including 68% of medics, 84.8% of veterinarians, and 72.2% of the public acknowledged that the current surveillance systems are ineffective and do not capture and share real-time information. More than 92% of the all respondents confirmed that mobile phones have the potential to improve health surveillance in the country. All respondents reported adhering to a nascent project of mobile phone-based health surveillance and confirmed that there is no existing similar approach in the country. The most preferred methods by all respondents for effective implementation of such platform are phone calls (96.92%) followed by SMS (49.23%) and smart phone digital forms (41.53%). This study revealed urgent needs of mobile phone technologies for health surveillance and interventions in Benin for real-time surveillance and efficient disease prevention.

Utilizing the Public GenomeTrakr Database for Foodborne Pathogen Traceback v1

2021 ◽  
Author(s):  
Ruth E Timme ◽  
Maria Sanchez ◽  
Marc Allard
Keyword(s):  
Real Time ◽  
Foodborne Pathogens ◽  
Pathogen Detection ◽  
Foodborne Pathogen ◽  
Reference Database ◽  
Surveillance Program ◽  
Environmental Isolates ◽  
The Public ◽  
The World ◽  
The Usa

This protocol outlines the all the steps necessary to become a GenomeTrakr data contributor. GenomeTrakr is an international genomic reference database of mostly food and environmental isolates from foodborne pathogens. The data and analyses are housed at the National Center for Biotechnology Information (NCBI), which is a database freely available to anyone in the world. The Pathogen Detection browser at NCBI computes daily cluster results adding the newly submitted data to the existing phylogenetic clusters of closely related genomes. Contributors to this database can see how their new isolates are related to the real-time foodborne pathogen surveillance program established in the USA and a few other countries, and at the same time adding valuable new data to the reference database. ------ Although originally published as a Chapter in Methods and Protocols, Foodborne Bacterial Pathogens, the protocol has since been adapted and split into four separate protocols all of which are contained in this collection.

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