Berrut, Catherine

Donnelly, Stephen

Marlow, Seán

Murphy, Noel

O'Connor, Noel E.

Smeaton, Alan F.

English

O\u27Connor, Noel E.

Irish Universities

Automatically detecting camera motion from MPEG-1 encoded video

Name not available

Automatically Detecting Camera Motionfrom MPEG-1 Encoded VideoStephen Donnelly, Alan F. Smeaton, Catherine BerrutSeán Marlow, Noel Murphy and Noel O’ConnorCentre for Digital Video ProcessingDublin City University, Dublin 9, Irelandasmeaton@compapp.dcu.ieKeywords: Digital Video; MPEG-1; Camera MotionWe have developed, implemented and tested a technique for detecting camera motion directly fromMPEG-1 encoded video that is based on processing the motion vectors in the bitstream.  Detecting cameramotion is equivalent to reverse engineering, from the video, in which direction the camera is turningduring a shot.  Camera motion parameters include the direction and magnitude of the camera turn, whetherthe motion is tracking, panning, booming or tilting and whether zoom-in or -out is being used.  Cameramotion forms a useful indexing feature for video in some applications such as logging news footage.   Inthe work reported here, we simply detect the direction of camera motion and other aspects are targeted asfuture work.Our system detects motion in real time as video is decoded and played based on the following process:1. A sequence parser extracts frames from an MPEG-1 video stream in the order in which the framesare to be played.2. We then extract macroblocks and their associated motion vectors; in MPEG-1 encoding there are396 (22x18) such blocks.  MPEG encoding is based on calculating the displacement ofmacroblocks between adjacent frames using a simple matching criterion, and representing thisdisplacement as a motion vector.  In our work we use an encoder from Optibase which has an I-frame (i.e. a video frame encoded as a still image, implying no motion vectors) every 12th frame.We extract motion vectors between I-frames and subsequent B-frames (i.e. video framespredictively encoded from previous and/or future frames). At a frame rate of 25 Hz, this means wesample camera motion about every 0.5 seconds.3. Motion vector filtering is then applied to eliminate any rogue vectors.  Rogue vectors are generallyunavoidable since they occur as a consequence of the fact that macroblock displacementcomputation is based on matching blocks of pixels as opposed to calculating real motion.   Wecategorise each non-zero motion vector into one of 90°, 180°, 270° and 360° bins as theserepresent the most common camera motion directions, certainly the ones of interest for indexing,and we use the fullest bin over a set threshold as the camera motion.Figure 1 shows the motion vectors foran I- to B-frame transition where themotion vectors are predominantly pointingto the left, indicating that the camera hasbeen panning to the left (note the roguevectors and areas of no motion).To date we have tested our approach on3 clips of video: a soccer match, anepisode of a soap opera, and a clip from aFormula 1 Grand Prix where we manuallymeasured the actual camera motion andcompared against the estimated motion. Ofthe frame transitions in this 3 minutes offootage we are correct for 85% of the time.Our future work will involve a largercollection of video, a more sophisticatedevaluation measure, the inclusion ofmotion vector magnitude as well asdirection, and the development andevaluation of more sophisticated approaches to camera motion detection and classification.

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Automatically detecting camera motion from MPEG-1 encoded video

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