Mesh-based piecewise planar motion compensation and optical flow clustering for ROI coding

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Meuel, H.; Munderloh, M.; Reso, M.; Ostermann, J.: Mesh-based piecewise planar motion compensation and optical flow clustering for ROI coding. In: APSIPA Transactions on Signal and Information Processing 4 (2015), e13. DOI: https://doi.org/10.1017/ATSIP.2015.12

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/4854

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Sum total of downloads: 98




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For the transmission of aerial surveillance videos taken from unmanned aerial vehicles (UAVs), region of interest (ROI)-based coding systems are of growing interest in order to cope with the limited channel capacities available. We present a fully automatic detection and coding system which is capable of transmitting high-resolution aerial surveillance videos at very low bit rates. Our coding system is based on the transmission of ROI areas only. We assume two different kinds of ROIs: in order to limit the transmission bit rate while simultaneously retaining a high-quality view of the ground, we only transmit new emerging areas (ROI-NA) for each frame instead of the entire frame. At the decoder side, the surface of the earth is reconstructed from transmitted ROI-NA by means of global motion compensation (GMC). In order to retain the movement of moving objects not conforming with the motion of the ground (like moving cars and their previously occluded ground), we additionally consider regions containing such objects as interesting (ROI-MO). Finally, both ROIs are used as input to an externally controlled video encoder. While we use GMC for the reconstruction of the ground from ROI-NA, we use meshed-based motion compensation in order to generate the pelwise difference in the luminance channel (difference image) between the mesh-based motion compensated and the current input image to detect the ROI-MO. High spots of energy within this difference image are used as seeds to select corresponding superpixels from an independent (temporally consistent) superpixel segmentation of the input image in order to obtain accurate shape information of ROI-MO. For a false positive detection rate (regions falsely classified as containing local motion) of less than 2. we detect more than 97. true positives (correctly detected ROI-MOs) in challenging scenarios. Furthermore, we propose to use a modified high-efficiency video coding (HEVC) video encoder. Retaining full HDTV video resolution at 30 fps and subjectively high quality we achieve bit rates of about 0.6-0.9Mbit/s, which is a bit rate saving of about 90. compared to an unmodified HEVC encoder.
License of this version: CC BY-NC-ND 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2015
Appears in Collections:Fakultät für Elektrotechnik und Informatik

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pos. country downloads
total perc.
1 image of flag of Germany Germany 40 40.82%
2 image of flag of United States United States 37 37.76%
3 image of flag of Russian Federation Russian Federation 7 7.14%
4 image of flag of Ukraine Ukraine 4 4.08%
5 image of flag of Czech Republic Czech Republic 4 4.08%
6 image of flag of Taiwan Taiwan 1 1.02%
7 image of flag of United Kingdom United Kingdom 1 1.02%
8 image of flag of France France 1 1.02%
9 image of flag of Finland Finland 1 1.02%
10 image of flag of Austria Austria 1 1.02%
    other countries 1 1.02%

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