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Emmanouil Michail

Researcher at Aristotle University of Thessaloniki

Publications -  16
Citations -  508

Emmanouil Michail is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Centroblasts & Drone. The author has an hindex of 9, co-authored 16 publications receiving 342 citations. Previous affiliations of Emmanouil Michail include Information Technology Institute.

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Book ChapterDOI

VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results

Pengfei Zhu, +104 more
TL;DR: A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
Proceedings ArticleDOI

EEG and HRV markers of sleepiness and loss of control during car driving

TL;DR: It is demonstrated that power spectral analysis of drivers' heart rate can report driving errors caused by sleepiness, and variation of Fractal Dimension (FD) can aid significant information for the assessment of the driving situation.
Book ChapterDOI

VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results

TL;DR: A large-scale video object detection and tracking dataset, which consists of 79 video clips with about 1.5 million annotated bounding boxes in 33, 366 frames, and the evaluation protocol of the VisDrone-VDT2018 challenge and the results of the algorithms on the benchmark dataset are presented.
Book ChapterDOI

VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results

Longyin Wen, +72 more
TL;DR: The evaluation protocol of the VisDrone-SOT2018 challenge is presented and the results of a comparison of 22 trackers on the benchmark dataset are presented, which are publicly available on the challenge website.

Visual and Textual Analysis of Social Media and Satellite Images for Flood Detection @ Multimedia Satellite Task MediaEval 2017.

TL;DR: Visual and textual analysis, as well as late fusion of their similarity scores, were deployed in social media images, while color analysis in the RGB and nearinfrared channel of satellite images was performed in order to discriminate flooded from non-flooded images.