Multi-Label Crowd Size And Location Recognition From Images

2020 ◽  
Author(s):  
Aamir Shabbir Hussain ◽  
Elena Pirogova ◽  
Margaret Lech
Keyword(s):  
Location Recognition ◽  
Crowd Size

scholarly journals Rich Common Crucial Feature for Crowdsourcing Based Mobile Visual Location Recognition

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hao Wang ◽  
Yugui Wang ◽  
Rui Cui ◽  
Yibo Han ◽  
Chaohua Yan ◽  
...  
Keyword(s):  
Location Recognition ◽  
Crucial Feature

scholarly journals Ontogeny of object versus location recognition in the rat: Acquisition and retention effects

2014 ◽  
Vol 56 (7) ◽  
pp. 1492-1506 ◽  
Author(s):  
Sara R. Westbrook ◽  
Lauren E. Brennan ◽  
Mark E. Stanton
Keyword(s):  
Location Recognition

Mass Gathering Events: Retrospective Analysis of Patient Presentations over Seven Years

2002 ◽  
Vol 17 (3) ◽  
pp. 147-150 ◽  
Author(s):  
Kathryn M. Zeitz ◽  
David P.A. Schneider ◽  
Dannielle Jarrett ◽  
Christopher J. Zeitz
Keyword(s):  
Formal Analysis ◽  
Presentation Rate ◽  
Evidence Base ◽  
First Aid ◽  
Event Analysis ◽  
Mass Gathering ◽  
Medical Problems ◽  
Maximum Daily Temperature ◽  
Day Of The Week ◽  
Crowd Size

AbstractIntroduction:St John Ambulance Operations Branch Volunteers have been providing first-aid services at the Royal Adelaide Show for 90 years. The project arose from a need to more accurately predict the workload for first-aid providers at mass gathering events. A formal analysis of workload patterns and the determinants of workload had not been performed.Hypothesis:Casualty presentation workload would be predicted by factors including day of the week, weather, and crowd size.Method:Collated and analyzed casualty reports over a seven-year period representing >7,000 patients who presented for first-aid assistance for that period (63 show days) were reviewed retrospectively.Results:Casualty presentations correlated significantly with crowd size, maximum daily temperature, humidity, and day of the week. Patient presentation rate had heterogeneous determinants. The most frequent presentation was minor medical problems with Wednesdays attracting higher casualty presentations and more major medical categories.Conclusion:Individual event analysis is a useful mechanism to assist in determining resource allocation at mass gathering events providing an evidence base upon which to make decisions about future needs. Subsequent analysis of other events will assist in supporting accurate predictor models.

scholarly journals Effects of Crowd Size, Composition, and Noise Level On Pool Use in a Mixed-Species Penguin Colony

2021 ◽  
Vol 8 (4) ◽  
pp. 507-520
Author(s):  
Ashley N. Edes ◽  
Eli Baskir ◽  
Karen L. Bauman ◽  
Nathasha Chandrasekharan ◽  
Michael Macek ◽  
...  
Keyword(s):  
Size Composition ◽  
Neutral Stimulus ◽  
Noise Levels ◽  
Mixed Species ◽  
Adults And Children ◽  
Human Adults ◽  
Penguin Colony ◽  
Crowd Size ◽  
Human Care ◽  
Human Hearing

Studies on how visitors affect penguins in human care report a mixture of negative, neutral, and positive impacts on behavior and physiology. Swimming is a highly motivated behavior that may promote positive welfare in penguins. We investigated how visitor crowd size, composition, and noise levels impact pool use in a mixed-species colony housing king (Aptenodytes patagonicus; n = 20), gentoo (Pygoscelis papua; n = 14), and southern rockhopper (Eudyptes chrysocome; n = 24) penguins. We used video and sound loggers to record if penguins were on land or in water, the number of human adults and children present, and noise levels using 5-minute scan samples from 09:00-15:00 over 36 continuous days. Data were analyzed using linear mixed models with proportion of penguins in the water as the dependent variable and crowd size, composition, and noise levels in A-weighted (dBA) and C-weighted (dBC) scales as independent variables. Crowd size was positively associated with pool use in gentoo penguins. Crowd composition did not predict pool use in any species. Noise levels in dBA, which is adjusted to the higher frequencies of human hearing, positively predicted pool use in southern rockhopper penguins. Noise levels in dBC, which captures lower frequencies, did not predict pool use in any species. No evidence of negative visitor effects was observed. Instead, these results suggest visitors are a neutral stimulus to king penguins and may be enriching to gentoo and southern rockhopper penguins.

scholarly journals Smartphone Location Recognition: A Deep Learning-Based Approach

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 214 ◽  
Author(s):  
Itzik Klein
Keyword(s):  
Deep Learning ◽  
Sampling Rate ◽  
Dead Reckoning ◽  
Step Length ◽  
Point Of View ◽  
Learning Approaches ◽  
Location Recognition ◽  
Length Estimation ◽  
Recorded Data ◽  
Definition Of

One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone’s accelerometers.

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