Adherent Raindrop Detection
(2018) In Master's Theses in Mathematical Sciences FMAM05 20181Mathematics (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:
http://lup.lub.lu.se/student-papers/record/8959055
- author
- Ulfwi, Björn LU
- supervisor
- organization
- course
- FMAM05 20181
- year
- 2018
- 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}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Adherent Raindrop Detection}}, year = {{2018}}, }