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Authors: Marcus Laumer 1 ; Peter Amon 2 ; Andreas Hutter 2 and André Kaup 3

Affiliations: 1 University of Erlangen-Nuremberg and Siemens Corporate Technology, Germany ; 2 Siemens Corporate Technology, Germany ; 3 University of Erlangen-Nuremberg, Germany

ISBN: 978-989-8565-47-1

Keyword(s): H.264/AVC, Compressed Domain, Object Detection, Macroblock Type.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Motion, Tracking and Stereo Vision ; Segmentation and Grouping ; Video Surveillance and Event Detection

Abstract: This paper introduces a low complexity frame-based object detection algorithm for H.264/AVC video streams. The method solely parses and evaluates H.264/AVC macroblock types extracted from the video stream, which requires only partial decoding. Different macroblock types indicate different properties of the video content. This fact is used to segment a scene in fore- and background or, more precisely, to detect moving objects within the scene. The main advantage of this algorithm is that it is most suitable for massively parallel processing, because it is very fast and combinable with several other pre- and post-processing algorithms, without decreasing their performance. The actual algorithm is able to process about 3600 frames per second of video streams in CIF resolution, measured on an Intel R CoreTM i5-2520M CPU @ 2.5 GHz with 4 GB RAM.

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Paper citation in several formats:
Laumer M., Amon P., Hutter A. and Kaup A. (2013). Compressed Domain Moving Object Detection based on H.264/AVC Macroblock Types.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 219-228. DOI: 10.5220/0004296602190228

@conference{visapp13,
author={Marcus Laumer and Peter Amon and Andreas Hutter and André Kaup},
title={Compressed Domain Moving Object Detection based on H.264/AVC Macroblock Types},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={219-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004296602190228},
isbn={978-989-8565-47-1},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Compressed Domain Moving Object Detection based on H.264/AVC Macroblock Types
SN - 978-989-8565-47-1
AU - Laumer M.
AU - Amon P.
AU - Hutter A.
AU - Kaup A.
PY - 2013
SP - 219
EP - 228
DO - 10.5220/0004296602190228

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