Georgios Kapidis
Papers
2
Total Citations
13
H-Index
2
About
Georgios Kapidis is a researcher whose work sits at the intersection of egocentric vision, activity recognition, and location classification. His research focuses on enabling wearable and autonomous systems to understand human context from first-person video, a technology with applications in life-logging, sports recording, and Ambient Assisted Living. Kapidis’s most cited paper, “Object Detection-Based Location and Activity Classification from Egocentric Videos: A Systematic Analysis” (2019), systematically evaluates how object detection can improve both where a person is and what they are doing. In his earlier foundational work, “Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric Videos” (2018), he directly compared deep learning architectures for spatial understanding, demonstrating that recurrent models can capture temporal cues for location inference. Together, these contributions have garnered over a dozen citations, establishing a methodological baseline for researchers building context-aware wearable systems. Kapidis’s work is notable for bridging computer vision and human-centric AI, offering practical frameworks for interpreting egocentric data in real-world assistive technologies.
Research Focus
Key Achievements
Top Papers
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