My work is focused on Computer Vision and Machine Learning
I am currently a full-time PhD student at Utrecht University's Department of Information and Computing Sciences. My project is based on Machine Learning and Computer Vision for human action and interaction recognition in everyday social settings, supervised by Dr. Ronald Poppe and Prof. Remco Veltkamp. I am also working on improvements on the efficiency and explainabilty of spatio-temporal Deep Learning and Artificial Intelligence methods, in scene understanding and classification of videos.
Before my PhD, I obtained my Masters in Advance Computer Science from University of Essex working with Dr. Adrian Clark. During this time I also worked at the Institute for Analytics and Data Science (IADS) for a summer project on Neural Networks for structured population data with Dr. Spyros Samothrakis. My Bachelor was also at the University of Essex in Computer Science.
A full list can be found in my GoogleScholar profile.
Alexandros Stergiou and Ronald Poppe
Pattern Recognition Letters, 2021
@article{stergiou2020learn,
title = {Learn to cycle: Time-consistent feature discovery for action recognition},
author={Stergiou, Alexandros and Poppe, Ronald},
journal = {Pattern Recognition Letters},
volume ={141},
pages ={1--7},
year ={2021},
issn ={0167-8655}}
Alexandros Stergiou, Ronald Poppe, Remco Veltkamp
Applied Sciences, 2020
@article{stergiou2020learning,
title={Learning Class-Specific Features with Class Regularization for Videos},
author={Stergiou, Alexandros and Poppe, Ronald and Veltkamp, Remco C},
journal={Applied Sciences},
volume={10},
number={18},
pages={6241},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute}}
Alexandros Stergiou, Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas,
Ronald Poppe, Remco Veltkamp
Intenrantional Conference on Computer Vision Workshop (ICCVW), 2019
@article{stergiou2019class,
title={Class Feature Pyramids for Video Explanation},
author={Stergiou, Alexandros and Kapidis, Georgios and Kalliatakis, Grigorios and
Chrysoulas, Christos Poppe, Ronald and Veltkamp, Remco},
booktitle={IEEE Intenrantional Conference on Computer Vision Workshops (ICCVW)},
year={2019},
organization={IEEE}}
Alexandros Stergiou, Ronald Poppe
Intenrantional Conference on Machine Learning Applications (ICMLA), 2019
@article{stergiou2019fast,
title={Spatio-Temporal FAST 3D Convolutions for Human Action Recognition},
author={Stergiou, Alexandros and Poppe, Ronald},
booktitle={IEEE Intenrantional Conference on Machine Learning Applications (ICMLA)},
year={2019},
organization={IEEE}}
Alexandros Stergiou, Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas,
Remco Veltkamp, Ronald Poppe
International Confernce on Image Procesing (ICIP), 2019
@article{stergiou2019saliency,
title={Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions},
author={Stergiou, Alexandros and Kapidis, Georgios and Kalliatakis, Grigorios and
Chrysoulas, Christos and Veltkamp, Remco and Poppe, Ronald},
booktitle={IEEE International Conference on Image Processing (ICIP)},
year={2019},
organization={IEEE}}
Alexandros Stergiou, Ronald Poppe
Computer Vision and Image Understanding, 2019
@article{stergiou2019analyzing,
author = {Stergiou, Alexandros and Poppe, Ronald},
title = {Analyzing human-human interactions: a survey},
journal = {Computer Vision and Image Understanding},
volume = {188},
pages = {102799},
year = {2019},
issn = {1077-3142}}
Grigorios Kalliatakis, Alexandros Stergiou, Nikolaos Vidakis
Computers, 2017
@article{computers6030025,
author = {Kalliatakis, Grigorios and Stergiou, Alexandros and Vidakis, Nikolaos},
title = {Conceiving Human Interaction by Visualising Depth Data of Head Pose Changes and Emotion Recognition via Facial Expressions},
journal = {Computers},
year = {2017}}
Lecturer for the Deep Learning lectures
Lecturer and Instructor for the Deep Learning/Neural Networks lectures
Instructor for the Deep Learning/Neural Networks lectures
These are some of my latest projects and code.
A package made as a solution to the problem of using video inputs in Machine Learning models.
As extracting and storing frames in .JPEG/.PNG files will quickly increase the memory requirements and more importantly the number of inodes.
This package provides a convenient alternative. Video frames are stored as blobs at database files .db which can be read as quickly as the .JPEG files
but without the additional large memory requirements.
code
An extension of separable convolutions for 3D volumes.
Performs volumetric convolutions for each channel of the input volume and will increase the output volume
based on the number of convolutional operations.
code
This project presents the use of Bootstrapping (or also known as bagging), to normalise the features of the polling population in order to represent the entire population that is eligible to vote. Additionally, for representing connections between individual features during
bootstrapping, a nearest neighbour method is employed in order for the newly created examples to resemble others from the general population. This technique will be applied to data from the 2016 Referendum that held the question of the remain or exit of the United Kingdom from the European Union.
PDF code
© Alexandros Stergiou