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Visual Crowd Counting System Using Deep Learning

Mohd Wafi Nazrul Adam, Mohd Ridhwan Oxley Adam (2021) Visual Crowd Counting System Using Deep Learning. Final Year Project, UTAR.

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    This project is about developing a visual crowd counting system using deep learning. The entirety of this project will only be using Python for both the back-end and the front-end development. The goal of this project is to develop a working system that could take in images and estimate the number of crowds in those images as well as display it’s estimated density map and a graph of predicted count against its ground truth as well as its accuracy in Mean Absolute Error (MAE) and Mean Squared Error (MSE). The back-end will be using a neural network model based on the Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (Zhang, et al., 2016) and is developed through the PyTorch framework, an open-source machine learning library. The model will be trained using the Mall Dataset and the Adam optimization algorithm. The trained model has an accuracy of 2.45 in MAE and 9.72 in MSE when tested using the Test portion of the dataset. The front-end is developed from scratch using the PyQT5 toolkit and QtDesigner.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: T Technology > TA Engineering (General). Civil engineering (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Information Technology (Honours) Computer Engineering
    Depositing User: ML Main Library
    Date Deposited: 06 Jan 2022 21:02
    Last Modified: 06 Jan 2022 21:02
    URI: http://eprints.utar.edu.my/id/eprint/4286

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