SHORT BIO

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Christos Kyrkou is a Research Lecturer at the KIOS Research & Innovation Center of Excellence at the University of Cyprus. Dr. Kyrkou received his Ph.D. degree in Computer Engineering from the University of Cyprus in 2014. He graduated top of his class and received his B.Sc in Computer Engineering in 2008 from the University of Cyprus . After been awarded a full scholarship he received his M.Sc. in Computer Engineering from the University of Cyprus in 2010. He is an author/co-author of more than 50 scientific publications in international peer reviewed conferences and journals, including 2 best paper awards. In 2020 he worked as teaching staff at the Department of Electrical and Computer Engineering at UCY teaching the course ECE627 Computer VIsion. He has worked in several National and European funded research and innovation projects. His research interests lie in the areas of Computer Vision and Deep Learning with emphasis on real-time performance. In particular efficient neural network architectures and techniques, algorithms for visual object recognition and detection, with applications in emergency management, autonomous vehicles, and drones. He serves as a reviewer for major conferences in the areas of computer vision and machine learning such as CVPR, NeurIPS, and ICML.

Edge Visual AI

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"Through my research I aspire to develop efficient, & robust Computer Vision and Deep Learning algorithms for embedded systems (e.g. advanced robots, autonomous cars, drones or Internet of Things applications) that enable holistic real-time scene understanding leading to more intelligent systems"

EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring

YOLOpeds: Efficient real-time single-shot pedestrian detection for smart camera applications

C^3 Net: End-to-end deep learning for active smart camera control

Deep Behavioral Cloning for Autonomous Vehicles

RECENT ARTICLES

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EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring

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UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire. 📰 📘 🎥 📦 💻 🎮Live Demo
IEEE JSTARS 2020

YOLOpeds: Efficient real-time single-shot pedestrian detection for smart camera applications

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This work addresses the challenge of achieving a good trade-off between accuracy and speed for efficient deep-learning-based pedestrian detection in smart camera applications.

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IET CV 2020

DeepCamera: Following Targets Without Bounding Boxes… End-to-end active vision

“We propose a supervised learning technique to control active cameras with Deep Convolutional Neural Networks to go directly from visual information to camera movement..”

READ POST ON MEDIUM 𝚳 📰

UPCOMING EVENTS

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CONNECT WITH ME ON SOCIAL MEDIA & PLATFORMS

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