are commonly used as features in speech recognition systems the algorithms below have a pre analysis component that extracts a subset of candidate frames from the stream working with a subset of frames is important to meet the real time operation requirement the frames are chosen based on a time division or a group of pictures gop boundary action scenes are detected based on audio signal amplitudes and motion vector magnitudes the relevant premise is that most action scenes usually have elevated sound accompanying them select a subset of predicted p frames from the stream based on time or gop boundaries for each selected frame decode the corresponding audio frames audio frames are associated via their pts value to the selected frame compute the rms value for each audio frame select frames that have the highest rms value extract the motion vectors action frames will be those that have the largest co located motion vector magnitudes this algorithm can be adapted to function on streams with no audio by considering only motion vectors fig3 a depicts some frames with overlaid motion vectors the motion vectors are mapped on the action frame note the motion vectors are located around the aircraft and clouds the sound of the aircraft accompanied the frame sequence low motion scenes are detected by analyzing macro block sizes and motion vector magnitudes fig3 b depicts that motion vectors and.