SHORT BIO

___________________

Christos Kyrkou is a Research Lecturer at the KIOS Research & Innovation Center of Excellence at the University of Cyprus. Dr. Kyrkou obtained his Ph.D. degree in Computer Engineering from the University of Cyprus in 2014. He completed his B.Sc. in Computer Engineering in 2008 graduating top of his class, and M.Sc. in Computer Engineering in 2010 where he was awarded a full scholarship, both from the University of Cyprus. He is an author/co-author of more than 60 scientific publications in international peer reviewed conferences and journals, including 2 best paper awards in the broaded areas of computing, machine learning, and computer vision. In particular, his research expertise are in the domains of Deep Learning and Vision with a strong focus on improving efficiency of recognition, detection, and segmentation algorithms with emphasis on real-time performance. Specifically, his research addresses the challenges of on-device AI through neural architecture improvement, and data-centric methodologies that allow for rapid and accurate data analysis and prediction tasks such as visual recognition and detection. Dr. Kyrkou has actively contributed to numerous National and European funded research and innovation projects with applications in emergency management, traffic monitoring, autonomous vehicles, and drones. Additionally, he is a frequent reviewer for esteemed conferences and periodicals in the areas of computer vision and machine learning , including CVPR, NeurIPS, and ICML.

Edge Vision and AI 

___________________

"Through my research I aspire to design efficient & robust Computer Vision and Deep Learning algorithms that enable holistic real-time perception and decision making 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 

AirCamRTM: Enhancing Vehicle Detection for Efficient Aerial Camera-based Road Traffic Monitoring

RECENT ARTICLES

___________________

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

________________

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

________________

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.

📚 


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

___________________


CONNECT WITH ME ON SOCIAL MEDIA & PLATFORMS

___________________