TY - GEN
T1 - Semantic segmentation of UAV aerial videos using convolutional neural networks
AU - Girisha, S.
AU - Manohara Pai, M. M.
AU - Verma, Ujjwal
AU - Pai, Radhika M.
PY - 2019/6
Y1 - 2019/6
N2 - Semantic segmentation of complex aerial videos enables a better understanding of scene and context. This enhances the performance of automated video processing techniques like anomaly detection, object detection, event detection and other applications. But, there is a limited study of semantic segmentation in aerial videos due to non-availability of the relevant dataset. To address this, an aerial video dataset is captured using DJI Phantom 3 professional drone and is annotated manually. In addition, the proposed research work investigates the performance of semantic segmentation algorithms for aerial videos implemented using Fully Convolution Networks (FCN) and U-net architectures. In this study, two classes (greenery, road) are considered for semantic segmentation. It is observed that both architectures perform competitively on the aerial videos of Unmanned Aerial Vehicle (UAV) with a pixel accuracy of 89.7% and 87.31% respectively.
AB - Semantic segmentation of complex aerial videos enables a better understanding of scene and context. This enhances the performance of automated video processing techniques like anomaly detection, object detection, event detection and other applications. But, there is a limited study of semantic segmentation in aerial videos due to non-availability of the relevant dataset. To address this, an aerial video dataset is captured using DJI Phantom 3 professional drone and is annotated manually. In addition, the proposed research work investigates the performance of semantic segmentation algorithms for aerial videos implemented using Fully Convolution Networks (FCN) and U-net architectures. In this study, two classes (greenery, road) are considered for semantic segmentation. It is observed that both architectures perform competitively on the aerial videos of Unmanned Aerial Vehicle (UAV) with a pixel accuracy of 89.7% and 87.31% respectively.
UR - http://www.scopus.com/inward/record.url?scp=85071441967&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071441967&partnerID=8YFLogxK
U2 - 10.1109/AIKE.2019.00012
DO - 10.1109/AIKE.2019.00012
M3 - Conference contribution
T3 - Proceedings - IEEE 2nd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019
SP - 21
EP - 27
BT - Proceedings - IEEE 2nd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019
Y2 - 3 June 2019 through 5 June 2019
ER -