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Relevant bibliographies by topics / Neural networks; Visual information / Dissertations / Theses

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Dissertations / Theses on the topic 'Neural networks; Visual information'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Song, Yue. "Towards Multi-Scale Visual Explainability for Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281359.

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Explainability methods seek to find out visual explanations for neural network decisions. Existing techniques mainly fall into two categories: backpropagation- based methods and occlusion-based methods. The former category selectively highlights the computed gradients, while the latter occludes the input to maximally confuse the classifier and visualize the distinct regions. Motivated by the occlusion methods, we propose an explainability model which to our knowledge is the first attempt to extract multi-scale explanations by perturbing the intermediate representations. Furthermore, we present

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2

Newman, Rhys A. "Automatic learning in computer vision." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390526.

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3

Mayer, Nikolaus [Verfasser], and Thomas [Akademischer Betreuer] Brox. "Synthetic training data for deep neural networks on visual correspondence tasks." Freiburg : Universität, 2020. http://d-nb.info/1216826692/34.

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4

Yavari, Najib. "Few-Shot Learning with Deep Neural Networks for Visual Quality Control: Evaluations on a Production Line." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283119.

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Having a well representative and adequate amount of data samples plays an important role in the success of deep learning algorithms used for image recognition. On the other hand, collecting and manually labeling a large-scale dataset requires a great deal of human interaction which in turn is very timeconsuming. In this thesis project, we explore the possibilities of new deeplearning approaches used for image recognition that do not require a big amount of data. Since Few-Shot Learning (FSL) models are known to be the most promising approach to tackle the problem of not having an adequate data

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5

Aboudib, Ala. "Neuro-inspired Architectures for the Acquisition and Processing of Visual Information." Thesis, Télécom Bretagne, 2016. http://www.theses.fr/2016TELB0419/document.

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L'apprentissage automatique et la vision par ordinateur sont deux sujets de recherche d'actualité. Des contributions clés à ces domaines ont été les fruits de longues années d'études du cortex visuel et de la fonction des réseaux cérébraux. Dans cette thèse, nous nous intéressons à la conception des architectures neuro-inspirées pour le traitement de l'information sur trois niveaux différents du cortex visuel. Au niveau le plus bas, nous proposons un réseau de neurones pour l'acquisition des signaux visuels. Ce modèle est étroitement inspiré par le fonctionnement et l'architecture de la retine

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6

Ajamlou, Kevin, and Max Sonebäck. "Multimodal Convolutional Graph Neural Networks for Information Extraction from Visually Rich Documents." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445457.

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Monotonous and repetitive tasks consume a lot of time and resources in businesses today and the incentive to fully or partially automate said tasks, in order to relieve office workers and increase productivity in the industry, is therefore high. One such task is to process and extract information from Visually Rich Documents (VRD:s), e.g., documents where the visual attributes contain important information about the contents of the document. A lot of recent studies have focused on information extraction from invoices, where graph based convolutional nerual networks have shown a lot of promise

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7

Michler, Frank [Verfasser], and Thomas [Akademischer Betreuer] Wachtler. "Self-Organization of Spiking Neural Networks for Visual Object Recognition / Frank Michler ; Betreuer: Thomas Wachtler." Marburg : Philipps-Universität Marburg, 2020. http://d-nb.info/1204199876/34.

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8

Dercksen, Vincent Jasper [Verfasser]. "Visual computing techniques for the reconstruction and analysis of anatomically realistic neural networks / Vincent Jasper Dercksen." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1081935391/34.

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9

Tong, Song. "Informatics Approaches for Understanding Human Facial Attractiveness Perception and Visual Attention." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/264679.

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京都大学<br>新制・課程博士<br>博士(情報学)<br>甲第23398号<br>情博第767号<br>新制||情||131(附属図書館)<br>京都大学大学院情報学研究科知能情報学専攻<br>(主査)教授熊田孝恒, 教授西田眞也, 教授齋木潤, 准教授延原章平<br>学位規則第4条第1項該当<br>Doctor of Informatics<br>Kyoto University<br>DFAM

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Salem, Tawfiq. "Learning to Map the Visual and Auditory World." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/86.

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The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Billions of images that capture this complex relationship are uploaded to social-media websites every day and often are associated with precise time and location metadata. This rich source of data can be beneficial to improve our understanding of the globe. In this work, we propose a general framework that uses these publicly available images for constructing dense maps of different ground-level attributes from overhead imagery. In particular, we use well-defined probabil

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11

Jiu, Mingyuan. "Spatial information and end-to-end learning for visual recognition." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0038/document.

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Dans cette thèse nous étudions les algorithmes d'apprentissage automatique pour la reconnaissance visuelle. Un accent particulier est mis sur l'apprentissage automatique de représentations, c.à.d. l'apprentissage automatique d'extracteurs de caractéristiques; nous insistons également sur l'apprentissage conjoint de ces dernières avec le modèle de prédiction des problèmes traités, tels que la reconnaissance d'objets, la reconnaissance d'activités humaines, ou la segmentation d'objets. Dans ce contexte, nous proposons plusieurs contributions : Une première contribution concerne les modèles de ty

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12

Shunmugam, Nagarajan. "Operational data extraction using visual perception." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292216.

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The information era has led the manufacturer of trucks and logistics solution providers are inclined towards software as a service (SAAS) based solutions. With advancements in software technologies like artificial intelligence and deep learning, the domain of computer vision has achieved significant performance boosts that it competes with hardware based solutions. Firstly, data is collected from a large number of sensors which can increase production costs and carbon footprint in the environment. Secondly certain useful physical quantities/variables are impossible to measure or turns out to b

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13

Dang, Hieu. "Adaptive multiobjective memetic optimization: algorithms and applications." Journal of Cognitive Informatics and Natural Intelligence, 2012. http://hdl.handle.net/1993/30856.

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The thesis presents research on multiobjective optimization based on memetic computing and its applications in engineering. We have introduced a framework for adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the selection, clustering, and local refinements. A robust stopping criterion for AMMOA has also been introduced to solve non-linear and large-scale optimization problems. The framework has been implemented for different benchmark test problems with remarkable results. This thesis also presents two applications of these

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14

Hazarika, Subhashis. "Statistical and Machine Learning Approaches For Visualizing and Analyzing Large-Scale Simulation Data." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574692702479196.

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15

Finfando, Filip. "Indoor scene verification : Evaluation of indoor scene representations for the purpose of location verification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288856.

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When human’s visual system is looking at two pictures taken in some indoor location, it is fairly easy to tell whether they were taken in exactly the same place, even when the location has never been visited in reality. It is possible due to being able to pay attention to the multiple factors such as spatial properties (windows shape, room shape), common patterns (floor, walls) or presence of specific objects (furniture, lighting). Changes in camera pose, illumination, furniture location or digital alteration of the image (e.g. watermarks) has little influence on this ability. Traditional appr

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16

Baddeley, Roland. "Visual statistics using neural networks." Thesis, University of Stirling, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259833.

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17

Lee, Ji Young Ph D. Massachusetts Institute of Technology. "Information extraction with neural networks." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111905.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 85-97).<br>Electronic health records (EHRs) have been widely adopted, and are a gold mine for clinical research. However, EHRs, especially their text components, remain largely unexplored due to the fact that they must be de-identified prior to any medical investigation. Existing systems for de-identification rely on manual rules or features, which are time-consuming to develop and fine-tun

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Rabi, Gihad. "Visual speech recognition by recurrent neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0010/MQ36169.pdf.

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19

Sørngård, Bård. "Information Theory for Analyzing Neural Networks." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26773.

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The goal of this thesis was to investigate how information theory could be used to analyze artificial neural networks. For this purpose, two problems, a classification problem and a controller problem were considered. The classification problem was solved with a feedforward neural network trained with backpropagation, the controller problem was solved with a continuous-time recurrent neural network optimized with evolution.Results from the classification problem shows that mutual information might indicate how much a particular neuron contributes to the classification. Tracking these neurons&a

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Hodge, Victoria J. "Integrating information retrieval & neural networks." Thesis, University of York, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.247019.

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21

Folta, Kristian. "Neural mechanisms of lateralized visual information processing." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=973557702.

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Barraclough, Nicholas Edward. "The neural processing of visual motion information." Thesis, University of Nottingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395577.

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23

Cheetham, Emma. "The neural networks recruited during visual feature binding." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/68612/.

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The binding problem presents one of the most challenging questions in psychology and cognitive neuroscience, despite its seemingly effortless resolution in daily life. Binding of visual features begins with stimulation of peripheral receptors and ends with the emergence of a perceived object, yet many questions remain unanswered about the nature of the intervening mechanisms. The primary focus of this thesis was to elucidate neurocognitive processes that support binding of features into a coherent object. Experiment 1 sought to dissociate neural correlates of feature binding from spatial and t

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24

Olde, Scheper Tjeerd. "Chaos and information in dynamic neural networks." Thesis, Oxford Brookes University, 2002. https://radar.brookes.ac.uk/radar/items/e2a920c8-ff78-4ad6-adf3-8217d18c3b96/1/.

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This research attempts to identify and model ways to store information in dynamic, chaotic neural networks. The justification for this research is given by both biological as well as theoretical motivations [2, 27, 29, 46, 60, 107]. Firstly, there seems to be substantial support for the use of dynamic networks to study more complex and interesting behaviour. The artificial neural networks (ANN) have specific properties that define its order, such as size, type and function. Simply extending the ANN with complex non linear dynamics does not improve the memory performance of the network, it mod

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Smith, Julian P. "Neural networks, information theory and knowledge representation." Thesis, University of Edinburgh, 1996. http://hdl.handle.net/1842/20801.

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Lee, Hyo-Dong. "Visual tasks beyond categorization for training convolutional neural networks." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106095.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 21-23).<br>Humans can perceive a variety of visual properties of objects besides their category. In this paper, we explore- whether convolutional neural networks (CNNs) can also learn object-related variables. The models are trained for object position, size and pose, respectively, from synthetic images and tested on unseen held-out objects. First, we show that some object properties come "fo

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Oquab, Maxime. "Convolutional neural networks : towards less supervision for visual recognition." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE061.

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Les réseaux de neurones à convolution sont des algorithmes d’apprentissage flexibles qui tirent efficacement parti des importantes masses de données qui leur sont fournies pour l’entraînement. Malgré leur utilisation dans des applications industrielles dès les années 90, ces algorithmes n’ont pas été utilisés pour la reconnaissance d’image à cause de leurs faibles performances avec les images naturelles. C’est finalement grâce a l’apparition d’importantes quantités de données et de puissance de calcul que ces algorithmes ont pu révéler leur réel potentiel lors de la compétition ImageNet, menan

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Evans, Benjamin D. "Learning transformation-invariant visual representations in spiking neural networks." Thesis, University of Oxford, 2012. https://ora.ox.ac.uk/objects/uuid:15bdf771-de28-400e-a1a7-82228c7f01e4.

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This thesis aims to understand the learning mechanisms which underpin the process of visual object recognition in the primate ventral visual system. The computational crux of this problem lies in the ability to retain specificity to recognize particular objects or faces, while exhibiting generality across natural variations and distortions in the view (DiCarlo et al., 2012). In particular, the work presented is focussed on gaining insight into the processes through which transformation-invariant visual representations may develop in the primate ventral visual system. The primary motivation for

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Wu, Lizhong. "Speech processing with neural networks." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259529.

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30

Lester, Ben. "Attentional and Neural Manipulations of Visuospatial Contextual Information." Thesis, University of Oregon, 2013. http://hdl.handle.net/1794/12985.

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A critical function of the human visual system is to parse objects from the larger context of the environment, allowing for the identification of, and potential interaction with, those objects. The use of contextual information allows us to rapidly locate, identify, and interact with objects that appear in the environment. Contextual information can help specify an object's location within the environment (allocentric encoding) or with respect to the observer (egocentric encoding). Understanding how contextual information influences perceptual organization, and the neural systems that process

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Hallum, Luke Edward Graduate School of Biomedical Engineering Faculty of Engineering UNSW. "Prosthetic vision : Visual modelling, information theory and neural correlates." Publisher:University of New South Wales. Graduate School of Biomedical Engineering, 2008. http://handle.unsw.edu.au/1959.4/41450.

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Electrical stimulation of the retina affected by photoreceptor loss (e.g., cases of retinitis pigmentosa) elicits the perception of luminous spots (so-called phosphenes) in the visual field. This phenomenon, attributed to the relatively high survival rates of neurons comprising the retina's inner layer, serves as the cornerstone of efforts to provide a microelectronic retinal prosthesis -- a device analogous to the cochlear implant. This thesis concerns phosphenes -- their elicitation and modulation, and, in turn, image analysis for use in a prosthesis. This thesis begins with a comparative r

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32

Lowe, Scott Corren. "Decoding information from neural populations in the visual cortex." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28861.

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Visual perception in mammals is made possible by the visual system and the visual cortex. However, precisely how visual information is coded in the brain and how training can improve this encoding is unclear. The ability to see and process visual information is not an innate property of the visual cortex. Instead, it is learnt from exposure to visual stimuli. We first considered how visual perception is learnt, by studying the perceptual learning of contrast discrimination in macaques. We investigated how changes in population activity in the visual cortices V1 and V4 correlate with the change

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33

Franklin, D. R. "Neural networks for visual feedback control of an industrial robot." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599180.

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The majority of industrial robots in use today are configured by on-line programming at the start of each production run. The workpieces are located using precision indexing. The robots have little or no sensory input, other than joint position feedback, and are unable to operate in changing or loosely constrained environments. To overcome these constraints and to increase the range of practical applications, robots need to be able to apply adaptive intelligence to manufacturing operations. This calls for enhanced sensory capabilities. Vision systems have been introduced successfully into many

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34

McClure, Patrick. "Adapting deep neural networks as models of human visual perception." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278073.

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Deep neural networks (DNNs) have recently been used to solve complex perceptual and decision tasks. In particular, convolutional neural networks (CNN) have been extremely successful for visual perception. In addition to performing well on the trained object recognition task, these CNNs also model brain data throughout the visual hierarchy better than previous models. However, these DNNs are still far from completely explaining visual perception in the human brain. In this thesis, we investigated two methods with the goal of improving DNNs’ capabilities to model human visual perception: (1) dee

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35

Xie, Ning. "Towards Interpretable and Reliable Deep Neural Networks for Visual Intelligence." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1596208422672732.

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36

Folsom, Tyler C. "Neural networks modeling cortical cells for machine vision /." Thesis, Connect to this title online; UW restricted, 1994. http://hdl.handle.net/1773/6135.

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37

Stigeborn, Patrik. "Generating 3D-objects using neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230668.

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Enabling a 2D- to 3D-reconstruction is an interesting future service for Mutate AB, where this thesis is conducted. Convolutional neural networks (CNNs) is examined in different aspects, in order to give a realistic perception of what this technology is capable of. The task conducted, is the creation of a CNN that can be used to predict how an object from a 2D image would look in 3D. The main areas that this CNN is optimized for are Quality, Speed, and Simplicity. Where Quality is the output resolution of the 3D object, Speed is measured by the number of seconds it takes to complete a reconstr

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38

Gousseau, Clément. "Hyperparameter Optimization for Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272107.

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Training algorithms for artificial neural networks depend on parameters called the hyperparameters. They can have a strong influence on the trained model but are often chosen manually with trial and error experiments. This thesis, conducted at Orange Labs Lannion, presents and evaluates three algorithms that aim at solving this task: a naive approach (random search), a Bayesian approach (Tree Parzen Estimator) and an evolutionary approach (Particle Swarm Optimization). A well-known dataset for handwritten digit recognition (MNIST) is used to compare these algorithms. These algorithms are also

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Zhang, Shuyuan. "AlphaZero with Input Convex Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281860.

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Modelling and solving real-life problems using reinforcement learning (RL) approaches is a typical and important branch in the world of artificial intelligence (AI). For playing board games, AlphaZero has been proved to be successful in games such as Go, Chess, and Shogi against professional human players or other AI counterparts. The very basic components of AlphaZero algorithm are MCTS tree search and deep neural networks for state value and policy prediction. These deep neural networks are designed to fit the mapping function between a state and its value/policy to make the initialization o

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40

Berry, Ian Michael. "Data classification using unsupervised artificial neural networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390079.

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Mazurek, Mark. "Neural mechanisms for combining information in a visual discrimination task /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/10649.

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Kim, Daehyon. "Acquiring parking information by image processing and neural networks." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308978.

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Lagerhjelm, Linus. "Extracting Information from Encrypted Data using Deep Neural Networks." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-155904.

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In this paper we explore various approaches to using deep neural networks to per- form cryptanalysis, with the ultimate goal of having a deep neural network deci- pher encrypted data. We use long short-term memory networks to try to decipher encrypted text and we use a convolutional neural network to perform classification tasks on encrypted MNIST images. We find that although the network is unable to decipher encrypted data, it is able to perform classification on encrypted data. We also find that the networks performance is depending on what key were used to en- crypt the data. These finding

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Molter, Colin. "Storing information through complex dynamics in recurrent neural networks." Doctoral thesis, Universite Libre de Bruxelles, 2005. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211039.

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The neural net computer simulations which will be presented here are based on the acceptance of a set of assumptions that for the last twenty years have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First of all, neural networks and their dynamical behaviors in terms of attractors is the natural way adopted by the brain to encode information. Any information item to be stored in the neural net should be coded in some way or another in one of the dynamical attractors of the brain and retrieved by stimulating the net so as to trap its dynamics in

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45

Norrstig, Andreas. "Visual Object Detection using Convolutional Neural Networks in a Virtual Environment." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156609.

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Visual object detection is a popular computer vision task that has been intensively investigated using deep learning on real data. However, data from virtual environments have not received the same attention. A virtual environment enables generating data for locations that are not easily reachable for data collection, e.g. aerial environments. In this thesis, we study the problem of object detection in virtual environments, more specifically an aerial virtual environment. We use a simulator, to generate a synthetic data set of 16 different types of vehicles captured from an airplane. To study

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Masih, Lawrence. "Associative recall in multilayered logical neural networks." Thesis, Brunel University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235933.

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Allred, Sarah R. "The Neural basis of visual object perception /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/10645.

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Besharat, Pour Shiva. "Hierarchical sales forecasting using Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290892.

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Sales forecasting equips businesses with the essential basis for planning future investments, controlling costs, and production. This research is in cooperation with a property development company for the purpose of improving the accuracy of manual sales forecasting. The objective is to investigate the effects of using the underlying factors that affect the individual sales of the company in forecasting the company’s income. One approach uses an aggregation of the estimates of the individual sales to approximate the company’s income. This approach uses the underlying hierarchical factors of th

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Styren, Buster. "Uveal melanoma identification using artificial neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241086.

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Uveal melanoma is a deadly form of cancer that can develop from a uveal nevus in the eye fundus. By using deep convolutional networks this thesis aims to classify fundus images based on malignancy. A baseline model was compared against two state-of-the-art networks, Inceptionv3 and ResNet. The baseline model was trained using different gradient descent optimizers and image augmentations to find the best hyper parameters for the data. The state-of-the-art networks achieved comparable accuracy, with Inception-v3 achieving 0.912 AUC after training on 8360 samples. With 96% sensitivity, the same v

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Zamboni, Simone. "Pedestrian trajectory prediction with Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278818.

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Modelling the behaviour of pedestrians is essential in autonomous driving because consequences for misjudging the intentions of a pedestrian can be severe when dealing with vehicles. Therefore, for an autonomous vehicle to plan a safe and collision-free path, it is necessary not only to know the current position of nearby pedestrians but also their future trajectory. In literature, methods to approach the problem of pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. This thesis proposes a new approach

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