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

  • Holger Meuel (a1), Marco Munderloh (a1), Matthias Reso (a1) and Jörn Ostermann (a1)
Abstract

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 0.6–0.9 Mbit/s, which is a bit rate saving of about 90% compared to an unmodified HEVC encoder.

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Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Corresponding author
Corresponding author: H. Meuel Email: meuel@tnt.uni-hannover.de
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[1] B. Ciubotaru ; G. Muntean ; G. Ghinea : Objective assessment of region of interest-aware adaptive multimedia streaming quality. IEEE Trans. Broadcast., 55 (2) (2009), 202212.

[3] N. Doulamis ; A. Doulamis ; D. Kalogeras ; S. Kollias : Low bit-rate coding of image sequences using adaptive regions of interest. IEEE Trans. Circuits Syst. Video Technol., 8 (8) (1998), 928934.

[4] M.-J. Chen ; M.-C. Chi ; C.-T. Hsu ; J.-W. Chen : ROI video coding based on H.263+ with robust skin-color detection technique. IEEE Trans. Consum. Electron., 49 (3) (2003), 724730.

[7] Y. Liu ; Z.G. Li ; Y.C. Soh : Region-of-interest based resource allocation for conversational video communication of H.264/AVC. IEEE Trans. Circuits Syst. Video Technol., 18 (1) (2008), 134139.

[17] H. Sakaino : Video-based tracking, learning, and recognition method for multiple moving objects. IEEE Trans. Circuits Syst. Video Technol., 23 (10) (2013), 16611674.

[18] B. Dey ; M. Kundu : Robust background subtraction for network surveillance in H.264 streaming video. IEEE Trans. Circuits Syst. Video Technol., 23 (10) (2013), 16951703.

[20] X. Zhang ; Y. Tian ; T. Huang ; S. Dong ; W. Gao : Optimizing the hierarchical prediction and coding in HEVC for surveillance and conference videos with background modeling. IEEE Trans. Image Process., 23, (10) (2014), 45114526.

[21] R. Jones ; B. Ristic ; N. Redding ; D. Booth : Moving target indication and tracking from moving sensors, in Proc. of Digital Image Comput.: Techniques and Application (DICTA), December 2005, 46.

[22] A. Shastry ; R. Schowengerdt : Airborne video registration and traffic-flow parameter estimation. IEEE Trans. Intell. Transp. Syst., 6 (4) (2005), 391405.

[24] A. Ibrahim ; P.W. Ching ; G. Seet ; W. Lau ; W. Czajewski : Moving objects detection and tracking framework for UAV-based surveillance, in Fourth Pacific-Rim Symp. on Image and Video Technology (PSIVT), November 2010, 456461.

[27] M. Teutsch ; W. Kruger : Detection, segmentation, and tracking of moving objects in UAV videos, in Proc. of the IEEE Ninth Int. Conf. on Advanced Video and Signal-Based Surveillance (AVSS), September 2012, 313318.

[28] R. Kumar : Aerial video surveillance and exploitation. Proc. IEEE, 89 (10) (2001), 15181539.

[30] T.N. Mundhenk ; K.-Y. Ni ; Y. Chen ; K. Kim ; Y. Owechko : Detection of unknown targets from aerial camera and extraction of simple object fingerprints for the purpose of target reacquisition, in Proc. SPIE, vol. 8301, 2012, 83 010H–83 010H–14. [Online]. Available at: http://dx.doi.org/10.1117/12.906491.

[31] D. Comaniciu ; P. Meer : Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24 (5) (2002), 603619.

[32] J. Xiao ; H. Cheng ; H. Feng ; C. Yang : Object tracking and classification in aerial videos, in Proc. of the SPIE Automatic Target Recognition XVIII, vol. 6967, 2008, 696 711–696 711–9. [Online]. Available at: http://dx.doi.org/10.1117/12.777827.

[43] M.A. Fischler ; R.C. Bolles : Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24 (6) (1981), 381395. [Online]. Available at: http://dx.doi.org/10.1145/358669.358692.

[47] R.A. Dwyer : A faster divide-and-conquer algorithm for constructing delaunay triangulations. Algorithmica, 2 (1–4) (1987), 137151.

[49] T. Fawcett : An introduction to ROC analysis. Pattern Recogn. Lett., 27 (8) (2006), 861874. [Online]. Available at: http://dx.doi.org/10.1016/j.patrec.2005.10.010.

[58] G. Sullivan ; J. Ohm ; W.-J. Han ; T. Wiegand : verview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol., 22 (12) (2012), 16491668.

[61] P. Mainali ; Q. Yang ; G. Lafruit ; L. Van Gool ; R. Lauwereins : Robust low complexity corner detector. IEEE Trans. Circuits Syst. Video Technol., 21 (4) (2011), 435445.

[62] O. Chum ; J. Matas : Optimal randomized RANSAC. IEEE Trans. Pattern Anal. Mach. Intell., 30 (8) (2008), 14721482.

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APSIPA Transactions on Signal and Information Processing
  • ISSN: 2048-7703
  • EISSN: 2048-7703
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