Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154130
Title: Detecting the crowdedness of people by deep learning
Authors: Heng, Seng En
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Heng, S. E. (2021). Detecting the crowdedness of people by deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154130
Abstract: Crowd management is a crucial aspect in the pandemic. Rapid spread of infection occurs in areas with high human concentrations. Therefore, there is a need for controlling the number of people in a vicinity. Computer vision-based AI crowd counting has been an existing area of research that could be leveraged on to enhance current crowd management efforts. In this project, various methods of Convolutional Neural Network (CNN) based crowd counting were studied, inspiring a few prototypes to be developed. Of them, the vgg19csr1 showed performance increases against baseline methods in false positive rejection and in low to medium crowds. Even though it did not attain the highest overall MAE, it fulfilled the criteria for use in context of COVID-19 Singapore. The project then ends off with the development of a functional automatic crowd monitoring system based on this model, demonstrating that AI based crowd counting has the potential to enhance COVID-19 crowd management efforts.
URI: https://hdl.handle.net/10356/154130
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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