Open AccessEstimation of Crowd Density from UAV Images based on Deep Learning
Author(s) -
Sarita Chauhan
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.37324
Subject(s) - computer science , convolutional neural network , event (particle physics) , artificial intelligence , deep learning , variable (mathematics) , real time computing , machine learning , computer vision , mathematical analysis , physics , mathematics , quantum mechanics
Crowd monitoring is necessary to improve safety and controllable movements to minimize risk, especially in high crowded events, such as Kumbh Mela, political rallies, sports event etc. In this current digital age mostly crowd monitoring still relies on outdated methods such as keeping records, using people counters manually, and using sensors to count people at the entrance. These approaches are futile in situations where people's movements are completely unpredictable, highly variable, and complex. Crowd surveillance using unmanned aerial vehicles (UAVs), can help us solve these problems. The proposed paper uses a UAV on which an IP Camera will be attached to get media, we then use a convolutional neural network to learn a regression model for crowd counting, the model will be trained extensively by using three widely used crowd counting datasets, ShanghaiTech part A and part B, UCF-CC 50 and UCF-QNRF.