April Status Provide Rich Data Set Consistent Imagery Play Metadata Ground Truth Player Positions.

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Presentation transcript:

April Status Provide Rich Data Set Consistent Imagery Play Metadata Ground Truth Player Positions

April Status Consistent Imagery Coaching Video of 2007 GT football Season –The Sideline View was chosen because it showed all players on the field

April Status Consistent Imagery Zooms and Pans to keep all players in view –Tracking and Stabilization Issues Cuts to scoreboard after each play –Enables Play Detection –Enables Situation Awareness

April Status Consistent Imagery Hi Resolution Video Image Artifacts do exist

April Status Consistent Imagery Distribution –Send a common set of files, using indexing to identify all 886 individual plays 

April Status Play Metadata Metadata retrieved from proprietary system –Export metadata for each game as.txt file –Recombine as an MS Excel Spreadsheet

April Status Play Metadata 19 Attributes for 886 Plays Appended video frame information to metadata –Create MATLAB functions to recognize scoreboard frames FORMATION PERSONNEL GROUP MOTIONSPLAY CODE PLAY DESCRIPTION PASS/RUNRESULTPLAY RESULTGAINWHO DEFENSEGAME PLAY#DOWNDISTANCEFIELD POSITION HASHDRIVE #DRIVE PLAY#DRIVE RESULT + = AVI FILESTART_FRAMEEND_FRAME

April Status Play Metadata Understanding Metadata –Playbook –Coaching Assistants

April Status Play Metadata Culled 886 to 189 –MS Excel Pivot Table Static Formations and Standard Personnel Selected the top 40 play descriptions and their top 40 formations

April Status Play Metadata Play labels were too specific (too few instances) Created Taxonomy to facilitate play recognition based on categories –Run Plays Wide Left Middle Left Middle Right Wide Right NOTA –Pass Plays Roll Out Drop Back –Short –Combo »Smash »Y Curl »Option »CMBK »NOTA Combo –Deep Screen None Of The Above (NOTA)

April Status Play Metadata Combine Views into an Online Application –Central data location for play review and analysis

April Status Ground Truth Player Positions –Built using MATLAB –Trained 7 students for ~35 clicks per frame –Average 109 Frames per play –7-15 Reference points (yard lines, sidelines and hash marks) –22 Players plus 2 Officials (clicked on hip) –1 Ball (hard to detect) –Run plays tracked until ball reaches LOS –Pass plays tracked until receiver determined –572,250 clicks to date 150 plays 109 frames per play 35 clicks per frame -As of 5/21 83 plays clicked and distributed 70 plays clicked awaiting audit 36 assigned for clicking Manual Tracking Application

April Status Ground Truth Player Positions Ground Truth Data Files

April Status Ground Truth Player Positions –Built using MATLAB –Labels reference points, players, officials and ball –Check plays for proper labeling and major tracking inconsistencies –Annotate frames with ball actions –Audited data files distributed to all members of the CARVE team Manual Auditing Application

April Status Ground Truth Player Positions Annotations with player locations in image coordinates

April Status Ground Truth Player Positions Transfer Data to Ortho-Rectified Field –Use reference points to calculate an homography matrix (H) for each frame –Use H to rectify points onto a scaled field diagram + H +

April Status Ground Truth Player Positions Use Rectified Data as Input for Feature Recognition