Report on UG^2+ Challenge Track 1: Assessing Algorithms to Improve Video Object Detection and Classification from Unconstrained Mobility Platforms
How can we effectively engineer a computer vision system that is able to interpret videos from unconstrained mobility platforms like UAVs? One promising option is to make use of image restoration and enhancement algorithms from the area of computational photography to improve the quality of the unde...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
26.07.2019
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Subjects | |
Online Access | Get full text |
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Summary: | How can we effectively engineer a computer vision system that is able to
interpret videos from unconstrained mobility platforms like UAVs? One promising
option is to make use of image restoration and enhancement algorithms from the
area of computational photography to improve the quality of the underlying
frames in a way that also improves automatic visual recognition. Along these
lines, exploratory work is needed to find out which image pre-processing
algorithms, in combination with the strongest features and supervised machine
learning approaches, are good candidates for difficult scenarios like motion
blur, weather, and mis-focus -- all common artifacts in UAV acquired images.
This paper summarizes the protocols and results of Track 1 of the UG^2+
Challenge held in conjunction with IEEE/CVF CVPR 2019. The challenge looked at
two separate problems: (1) object detection improvement in video, and (2)
object classification improvement in video. The challenge made use of the UG^2
(UAV, Glider, Ground) dataset, which is an established benchmark for assessing
the interplay between image restoration and enhancement and visual recognition.
16 algorithms were submitted by academic and corporate teams, and a detailed
analysis of how they performed on each challenge problem is reported here. |
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DOI: | 10.48550/arxiv.1907.11529 |