Performance Analysis of Semantic Segmentation Algorithms for Finely Annotated New UAV Aerial Video Dataset (ManipalUAVid)

Girisha, S and Pai, Manohara M.M. and Verma, Ujwal and Pai, Radhika M (2019) Performance Analysis of Semantic Segmentation Algorithms for Finely Annotated New UAV Aerial Video Dataset (ManipalUAVid). IEEE Access, 7. pp. 136239-136253. ISSN 2169-3536

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Abstract

Semantic segmentation of videos helps in scene understanding, thereby assisting in other automated video processing techniques like anomaly detection, object detection, event detection, etc. However,there has been limited study on semantic segmentation of videos acquired using Unmanned Aerial Vehicles (UAV), primarily due to the absence of standard dataset. In this paper, a new UAV aerial video dataset (ManipalUAVid) for semantic segmentation is presented. The videos have been acquired in a closed university campus, and fineannotation is provided for four background classes viz.constructions,greeneries, roads, and waterbodies. Also, the performance of four semantic segmentation approaches: Conditional Random Field (CRF), U-Net, Fully Convolutional Network (FCN) and DeepLabV3+ are analysed on ManipalUAVid dataset. It is seen that these algorithms perform competitively on UAV aerial video dataset and achieves an mIoU of 0.86, 0.86, 0.86 and 0.83 respectively

Item Type: Article
Uncontrolled Keywords: Convolutional neural networks, semantic segmentation, shot boundary detection, UAV video.
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 12 Feb 2020 10:53
Last Modified: 12 Feb 2020 10:53
URI: http://eprints.manipal.edu/id/eprint/154915

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