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
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.