Law enforcement agents have to care about the number of people in public areas to ensure security. The problem they have is that they do not have tools to measure the number of people in a fast and precise way. This need has been especially important since 2020 COVID pandemic arrived to our society and the control of people is relevant to avoid spread of COVID. This Master Thesis is complementing other previous Master Thesis presented in 2021 where via an Android app connected to a drone the system was able to count people from the images captured in real time. This solution was only able to count individual people, as crowds of people are complex to measure following standard object detection algorithms as YOLO technology. In our Master Thesis we are adding a new functionality by being able not only to count individuals but also counting crowds of people. With this new functionality the app could provide to the police a more accurate tool to be able to count people in different scenarios as prides, sports events, demonstrations, concerts¿ where crowd is a normal situation. As main technology driver we are working with CNN (Convolutional Neural Networks). First, we have been implementing a CNN density map using the CSRNet technology that is able to count people by measuring the concentration of people. Therefore, an important part of this Master Thesis is to create a process to split the input images in 2 (segmentation process), one for YOLO (individual persons) and other for CSRNET (crowds of people). This process has been implemented using a second CNN called Region-based CNN (R-CNN), that we found it was the most suitable tool to train a model to detect a crowd. The solution has been developed in Google Colab platform and using Python as programming language. We have been working with images taken from drones from Castelldefels Police and UPC but also public datasets. The final solution has been able to detect crowds and calculate the number of people in that crowd with a maximum error of 20% considering Mean Average Percentage Error (MAPE) and 89 considering Mean Absolute Error (MAE).

Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles

Objectius de Desenvolupament Sostenible::3 - Salut i Benestar