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Adherent Raindrop Detection

Ulfwi, Björn LU (2018) In Master's Theses in Mathematical Sciences FMAM05 20181
Mathematics (Faculty of Engineering)
Abstract
Raindrops adhered to the glass protecting a surveillance camera can significantly degrade the visibility of a scene. The goal of this master's thesis is to develop an accurate, efficient, computationally cheap algorithm that automatically detects adherent raindrops using only video and then removes them by activating the wipers or the shaking dome function. Two already existing algorithms, Temporal Intensity Difference and Maximally Stable Extremal Regions, were tested. Furthermore, additional criteria were added to the existing algorithms to improve performance, such as requiring the raindrops to be detected when they land on the screen. An algorithm that detects lens flares was also developed. The algorithms were tested on common... (More)
Raindrops adhered to the glass protecting a surveillance camera can significantly degrade the visibility of a scene. The goal of this master's thesis is to develop an accurate, efficient, computationally cheap algorithm that automatically detects adherent raindrops using only video and then removes them by activating the wipers or the shaking dome function. Two already existing algorithms, Temporal Intensity Difference and Maximally Stable Extremal Regions, were tested. Furthermore, additional criteria were added to the existing algorithms to improve performance, such as requiring the raindrops to be detected when they land on the screen. An algorithm that detects lens flares was also developed. The algorithms were tested on common surveillance scenes during both day and night. Combining these criteria and algorithms proved to be better than already existing methods and still fulfils the requirement that the algorithms should be computationally cheap. The final algorithm on average found 49% of the drops present in the picture with a false detection rate of 6.7% and took on average 3.1 seconds to run. (Less)
Please use this url to cite or link to this publication:
author
Ulfwi, Björn LU
supervisor
organization
course
FMAM05 20181
year
type
H2 - Master's Degree (Two Years)
subject
keywords
adherent raindrops, rain removal, temporal intensity difference, mser, convolutional neural networks, image analysis, computer vision
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3364-2018
ISSN
1404-6342
other publication id
2018:E64
language
English
id
8959055
date added to LUP
2018-10-11 16:15:52
date last changed
2018-10-11 16:15:52
@misc{8959055,
  abstract     = {Raindrops adhered to the glass protecting a surveillance camera can significantly degrade the visibility of a scene. The goal of this master's thesis is to develop an accurate, efficient, computationally cheap algorithm that automatically detects adherent raindrops using only video and then removes them by activating the wipers or the shaking dome function. Two already existing algorithms, Temporal Intensity Difference and Maximally Stable Extremal Regions, were tested. Furthermore, additional criteria were added to the existing algorithms to improve performance, such as requiring the raindrops to be detected when they land on the screen. An algorithm that detects lens flares was also developed. The algorithms were tested on common surveillance scenes during both day and night. Combining these criteria and algorithms proved to be better than already existing methods and still fulfils the requirement that the algorithms should be computationally cheap. The final algorithm on average found 49% of the drops present in the picture with a false detection rate of 6.7% and took on average 3.1 seconds to run.},
  author       = {Ulfwi, Björn},
  issn         = {1404-6342},
  keyword      = {adherent raindrops,rain removal,temporal intensity difference,mser,convolutional neural networks,image analysis,computer vision},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Adherent Raindrop Detection},
  year         = {2018},
}