Dense Crowds Detection and Surveillance with Drones using Density Maps

Detecting and Counting people in a human crowd from a moving drone present challenging problems that arisefrom the constant changing in the image perspective andcamera angle. In this paper, we test two different state-of-the-art approaches, density map generation with VGG19 trainedwith the Bayes loss function and detect-then-count with FasterRCNN with ResNet50-FPN as backbone, in order to comparetheir precision for counting and detecting people in differentreal scenarios taken from a drone flight. We show empiricallythat both proposed methodologies perform especially well fordetecting and counting people in sparse crowds when thedrone is near the ground. Nevertheless, VGG19 provides betterprecision on both tasks while also being lighter than FasterRCNN. Furthermore, VGG19 outperforms Faster RCNN whendealing with dense crowds, proving to be more robust toscale variations and strong occlusions, being more suitable forsurveillance applications using dronesComment: 2020 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece, 202

PROTOTYPE APPLICATION OF CROWD DETECTION SYSTEM FOR TRADITIONAL MARKET VISITOR BASED ON IOT USING RFID MFRC522

Crowds of people are the government's concern in dealing with the COVID-19 pandemic because the virus transfers unwittingly from one person to another and transmits it to the closest environment. One of the locations where crowds are difficult to avoid is a traditional market and is thought to be one of the places that have the potential to become the center of the spread of COVID-19. Various efforts made by the government in suppressing crowds have yielded results, but not a few violations that occur are carried out intentionally or unintentionally, one of the efforts to prevent crowd violations is the traditional market visitor detection monitoring system by market management so that market visitors do not violate health protocols and crowds that occur in an area can be avoided. In this study, an IoT-based crowd detection system application prototype uses an RFID sensor MFRC522 as a crowd indicator based on data on the number of visitors entering a kiosk that is recorded in the database and then displayed on the application, this data becomes an indicator of which kiosk other visitors want to go to so that the crowd can be avoided. System functionality testing was carried out with 4 scenarios and system reliability testing through data transmission was carried out 10 times with test data in the form of kiosk id and visitor id sent via a single Transmission Control Protocol (TCP) with a full-duplex communication channel. The test results show that crowd indications can be detected in the application with data transmission speeds reaching 875 KB/s with an average delay of 231.4 ms and a standard deviation of 215 ± 313 ms

Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks

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