TY - GEN
T1 - Semantic Segmentation of UAV Videos based on Temporal Smoothness in Conditional Random Fields
AU - Girisha, S.
AU - Manohara Pai, M. M.
AU - Verma, Ujjwal
AU - Pai, Radhika M.
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/30
Y1 - 2020/10/30
N2 - Video semantic segmentation is increasingly becoming a vital factor in many Unmanned Aerial Vehicle (UAV) drone-based applications such as surveillance, scene understanding etc. However, the accuracy of video semantic segmentation systems are greatly dependent on temporal consistent labelling. In this regard, a new approach for semantic segmentation of UAV videos is proposed by utilizing U-Net and Conditional Random Field. This algorithm incorporates temporal information to ensure temporal consistency in labelling. This work shows that Conditional Random Field algorithm along with temporal cues reduces the false positives and increases the accuracy of semantic segmentation. Moreover, the proposed method is quantitatively evaluated on ManipalUAVid dataset and achieved a mIoU of 0.88 which is significantly greater than traditional image based segmentation method such as U-Net.
AB - Video semantic segmentation is increasingly becoming a vital factor in many Unmanned Aerial Vehicle (UAV) drone-based applications such as surveillance, scene understanding etc. However, the accuracy of video semantic segmentation systems are greatly dependent on temporal consistent labelling. In this regard, a new approach for semantic segmentation of UAV videos is proposed by utilizing U-Net and Conditional Random Field. This algorithm incorporates temporal information to ensure temporal consistency in labelling. This work shows that Conditional Random Field algorithm along with temporal cues reduces the false positives and increases the accuracy of semantic segmentation. Moreover, the proposed method is quantitatively evaluated on ManipalUAVid dataset and achieved a mIoU of 0.88 which is significantly greater than traditional image based segmentation method such as U-Net.
UR - https://www.scopus.com/pages/publications/85099712272
UR - https://www.scopus.com/pages/publications/85099712272#tab=citedBy
U2 - 10.1109/DISCOVER50404.2020.9278040
DO - 10.1109/DISCOVER50404.2020.9278040
M3 - Conference contribution
AN - SCOPUS:85099712272
T3 - 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings
SP - 241
EP - 245
BT - 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020
Y2 - 30 October 2020 through 31 October 2020
ER -