Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77320
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dc.contributor.authorLee, Ying Hui
dc.date.accessioned2019-05-27T02:49:30Z
dc.date.available2019-05-27T02:49:30Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/77320
dc.description.abstractPerson ReID is the problem of matching people across many different camera views, also known as multi-camera tracking. This is an important area of research due to its usefulness in public security applications. Compared to other machine learning problems such as product searching, person ReID is an extremely challenging task, especially in real-world environments. This is due to variances in training data, including illumination condition, resolution, different viewpoint, and partial occlusion of individuals. Therefore, the objective of this project was to build a person ReID system that trains a neural network on many public datasets (e.g. Market1501, DukeMTMC-reID) to seek the most discriminative projections of the image features extracted from the previous neural network layers to adapt to the NTUCampus dataset. This project focuses on triplet-based deep similarity learning and domain adaptation. In this project, different combinations of algorithms were tested on many datasets to find a suitable combination of algorithms that will be good for domain adaptation. From the experiments, it was shown that there was not one combination of algorithms that performed the best for domain adaptation. Instead, the model to choose will depend on which dataset the model is going to be tested on. For testing on NTUCampus dataset, the models that were trained without softmax performed relatively better than the models that were trained with softmax. This can be a consideration when conducting future experiments.en_US
dc.format.extent56 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleTraining convolutional neural networks for person re-identificationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorAlex Kot Chichungen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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