TY - GEN
T1 - An Efficient Video Desnowing and Deraining Method with a Novel Variant Dataset
AU - Sadeghzadeh, Arezoo
AU - Islam, Md Baharul
AU - Zaker, Reza
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Video desnowing/deraining plays a vital role in outdoor vision systems, such as autonomous driving and surveillance systems, since the weather conditions significantly degrade their performance. Although numerous approaches have been reported for video snow/rain removal, they are limited to a few videos and did not consider the variations that occurred for the camera and background in real applications. We build a complete snow and rain dataset to overcome this limitation, consisting of 577 videos with synthetic snow and rain, quasi-snow, and real snow and rain. All possible variations of the background and the camera are considered in the dataset. Then, an efficient pixel-wise video desnowing/deraining method is proposed based on the color and temporal information in consecutive video frames. It is highly likely for a single pixel to be a background pixel rather than a snowy pixel at least once in the consecutive frames. Inspiring from this fact along with the color information of the snow pixels, we extract the background pixels from different consecutive frames by searching for the minimum gray-scale intensity. Experimental results demonstrate and validate the proposed method’s robustness to illumination and high-performance static background and camera.
AB - Video desnowing/deraining plays a vital role in outdoor vision systems, such as autonomous driving and surveillance systems, since the weather conditions significantly degrade their performance. Although numerous approaches have been reported for video snow/rain removal, they are limited to a few videos and did not consider the variations that occurred for the camera and background in real applications. We build a complete snow and rain dataset to overcome this limitation, consisting of 577 videos with synthetic snow and rain, quasi-snow, and real snow and rain. All possible variations of the background and the camera are considered in the dataset. Then, an efficient pixel-wise video desnowing/deraining method is proposed based on the color and temporal information in consecutive video frames. It is highly likely for a single pixel to be a background pixel rather than a snowy pixel at least once in the consecutive frames. Inspiring from this fact along with the color information of the snow pixels, we extract the background pixels from different consecutive frames by searching for the minimum gray-scale intensity. Experimental results demonstrate and validate the proposed method’s robustness to illumination and high-performance static background and camera.
KW - Deraining
KW - Desnowing
KW - Snow and rain dataset
KW - Static/dynamic background
KW - Synthetic/quasi snow
KW - Temporal information
UR - http://www.scopus.com/inward/record.url?scp=85115875303&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87156-7_16
DO - 10.1007/978-3-030-87156-7_16
M3 - Conference contribution
AN - SCOPUS:85115875303
SN - 9783030871550
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 195
EP - 208
BT - Computer Vision Systems - 13th International Conference, ICVS 2021, Proceedings
A2 - Vincze, Markus
A2 - Patten, Timothy
A2 - Christensen, Henrik I
A2 - Nalpantidis, Lazaros
A2 - Liu, Ming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Computer Vision Systems, ICVS 2021
Y2 - 22 September 2021 through 24 September 2021
ER -