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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 foreand 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 preand 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.

Compressed Domain Moving Object Detection based on H.264/AVC Macroblock Types