SHORT BIO

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Christos Kyrkou is a Senior Research Associate 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 is an author/co-author of more than 50 scientific publications in international peer reviewed conferences and journals. He was a visiting Lecturer at the Department of Electrical and Computer Engineering at UCY teaching the course ECE627 Computer VIsion. He has experience 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 embedded deep vision (efficient convolutional network architectures and techniques), visual object recognition and detection, vision applications, vision for robotics and autonomous vehicles.

Efficient and Embedded Vision, Deep Learning, 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

Per Lane Vehicle Counting and Flow Estimation

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. ๐Ÿ“ฐ ๐Ÿ“˜ ๐ŸŽฅ ๐Ÿ“ฆ ๐Ÿ’ป ๐ŸŽฎLive 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

Counting from the Air: Vehicle counting per Lane from on board a UAV for real-time traffic monitoring

โ€œThe flexibility and cost efficiency of traffic monitoring using Unmanned Aerial Vehicles (UAVs) has made such a proposition an attractive topic of research.โ€

READ POST ON MEDIUM ๐šณ ๐Ÿ“ฐ

UPCOMING EVENTS

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