Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations

Treball fi de màster de: Master in Intelligent Interactive Systems

Detalles Bibliográficos
Autor: Svetoslavov Hristov, Stefan
Tipo de recurso: tesis de maestría
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/68487
Acceso en línea:http://hdl.handle.net/10230/68487
Access Level:acceso abierto
Palabra clave:Aprenentatge profund
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spelling Fine-turning open-source deep learning models for crowd tracking in immersive interactive installationsSvetoslavov Hristov, StefanAprenentatge profundTreball fi de màster de: Master in Intelligent Interactive SystemsTutors: Rafael Redondo, Vivenç GómezThis paper explores the development of a real-time people tracking system for immersive interactive environments using open-source deep learning models. The goal was to create an AI-based solution capable of tracking people in complex environments where immersive interactive systems are placed, and varying lighting conditions are present. The research focuses on leveraging pre-trained models and fine-tuning them to meet specific application needs, rather than building a tracking system from scratch. The study employed a detection and tracking framework using the RTMDet-tiny detection model, fine-tuned with specific datasets, and integrated with the DeepSORT tracking algorithm. The refined model's performance was evaluated based on its accuracy and robustness in different sequences, considering metrics such as mean Average Precision (mAP), Higher Order Tracking Accuracy (HOTA), Multiple Object Tracking Accuracy (MOTA), and Identification F1 Score (IDF1). The refined detection model showed an overall average accuracy (mAP) of 0.741, with significant variations depending on object size and intersection over union (IoU) thresholds. The system performed well in detecting medium-sized objects but struggled with small and large objects due to the lack of annotated diverse training data. The discussion highlights the challenges and limitations encountered, such as the modular integration issues with OpenMMLab repositories and the high manual cost of data annotation. Future work should focus on enriching the training dataset with more varied images, implementing posture and body part detection, and exploring alternative tracking algorithms like ByteTrack for potentially better performance. Additionally, filtering detections at the edges of the image to reduce fluctuations and improving visual descriptors for distinguishing individuals in complex environments are suggested as important steps to enhance the system's accuracy and reliability. This study contributes to the field of Computer Vision by demonstrating the practical application of deep learning models in real-time people tracking for immersive interactive systems. The insights gained can inform future developments in AI-based tracking solutions, ensuring more engaging and personalized user experiences in various interactive settings.202420242024info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/68487reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésAttribution-NonCommercial- NoDerivs 3.0 Spainhttps://creativecommons.org/licenses/by-nc-nd/3.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/684872026-05-29T05:05:01Z
dc.title.none.fl_str_mv Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations
title Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations
spellingShingle Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations
Svetoslavov Hristov, Stefan
Aprenentatge profund
title_short Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations
title_full Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations
title_fullStr Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations
title_full_unstemmed Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations
title_sort Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations
dc.creator.none.fl_str_mv Svetoslavov Hristov, Stefan
author Svetoslavov Hristov, Stefan
author_facet Svetoslavov Hristov, Stefan
author_role author
dc.subject.none.fl_str_mv Aprenentatge profund
topic Aprenentatge profund
description Treball fi de màster de: Master in Intelligent Interactive Systems
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/68487
url http://hdl.handle.net/10230/68487
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial- NoDerivs 3.0 Spain
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial- NoDerivs 3.0 Spain
https://creativecommons.org/licenses/by-nc-nd/3.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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