Crowd control

2007 ◽  
Author(s):  
Debra Speert
Keyword(s):  
Crowd Control

scholarly journals Bacterial Crowd Control with Iron

2005 ◽  
Vol 12 (7) ◽  
pp. 721-723 ◽  
Author(s):  
Helen E. Blackwell
Keyword(s):  
Crowd Control

CROWD-CONTROL IN SIXTH-CENTURY CLONMACNOISE (ADOMNÁN, VC 1.3)

ÉRIU ◽  
2010 ◽  
Vol 60 (1) ◽  
pp. 131-136
Author(s):  
DAVID WOODS
Keyword(s):  
Sixth Century ◽  
Crowd Control

scholarly journals Heat Shock Factor 1: From Fire Chief to Crowd-Control Specialist

2016 ◽  
Vol 63 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Catherine G. Triandafillou ◽  
D. Allan Drummond
Keyword(s):  
Heat Shock ◽  
Heat Shock Factor ◽  
Heat Shock Factor 1 ◽  
Crowd Control

scholarly journals Crowd density estimation using deep learning for Hajj pilgrimage video analytics

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1190
Author(s):  
MD ROMAN BHUIYAN ◽  
Dr Junaidi Abdullah ◽  
Dr Noramiza Hashim ◽  
Fahmid Al Farid ◽  
Dr Jia Uddin ◽  
...  
Keyword(s):  
Absolute Error ◽  
Visual Surveillance ◽  
Small Space ◽  
Video Footage ◽  
Crowd Control ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Precise Analysis ◽  
Density Map ◽  
Control Study

Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This paper aims to propose an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.

Agent-based hardware-in-the-loop simulation for UAV/UGV surveillance and crowd control system

2013 ◽  
Author(s):  
Amirreza M. Khaleghi ◽  
Dong Xu ◽  
Alfonso Lobos ◽  
Sara Minaeian ◽  
Young-Jun Son ◽  
...  
Keyword(s):  
Control System ◽  
Hardware In The Loop ◽  
Agent Based ◽  
Crowd Control

Crowd-Control Agents

2005 ◽  
pp. 179-193
Author(s):  
Kari Blaho-Owens
Keyword(s):  
Crowd Control

Less-lethal weapons, crowd control and fourth generation conflicts

Inflexions ◽  
2007 ◽  
Vol N° 5 (1) ◽  
pp. 205
Author(s):  
Georges-Henri Bricet des Vallons
Keyword(s):  
Fourth Generation ◽  
Crowd Control
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