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  • pg 1
									Information Extraction from Cricket

                            Syed Ahsan Ishtiaque
                                    Kumar Srijan
            Problems Statement
• There can be many challenges like face detection
  and recognition, boundary detection, separating
  out the ad and the cricket part and many more.
• The problems we addressed are
  –   Shot Transition Detection
  –   Crowd Detection
  –   Pitch Detection
  –   Bowling Side Detection
  –   Ball by Ball segmentation
  –   Summary Making
      Shot Transition Detection
• A shot is defined as a sequence of frames
  captured by the camera in a contiguous way,
  without interruptions.
• Shots are of two types
  – Hard Transition
  – Soft Transition
• Following are our solution to this problem
     Normalized Cross Correlation
• Threshold the correlation between every 20th
   – By checking the correlation between every 20th frame, and if its
     greater than certain threshold assuming that some transition has
     occurred, then checking for correlation between every frame in that
     interval, if any correlation value is greater than another threshold,
     then Hard Transition else Soft Transition
• Results and Inferences
   – The results were satisfactory for Hard Transition, but got false matches
     for soft transition in the case when there was actually no transition
     and the camera was moving very fast, as the correlation value for the
     Kth and K+20th frame crossed the threshold.
   – Also there was a problem that the correlation between the frames of
     crowd was generally low, so generally every frame of crowd was
     classified as a shot transition
              Histogram Based
• Histogram based Detection
   – By calculating the Histogram for all the bands or
     for gray band, and thresholding for sharp change
     of values we can detect for a transition.
• Results and Inferences
   – This gave good results, the problem with this
     approach was that it was not taking the position
     of pixels into account
                       Hard Transition

                                              Hard Transition

                      Hard Transition

Every frame here is a hard transition, but the Histogram
         approach will not be able to detect this
                Difference of NCC
• Difference of correlation values between
  consecutive frames
   – In this approach we checked the difference of correlation, and if
     it is greater than a certain threshold we declare it as a hard
   – Also blurring of the frames improved correlation and hence the
• Results and Inferences
   – This also gave very good results, and by this approach the
     problem of classification of crowd frames generally as shot was
     also solved, as although the correlation between the frames of
     crowd was low, but difference of correlation between the two
     low correlation frame groups was not that high, so it solved the
            Crowd Detection
• Detecting the scenes in which crowd is
  present or is in focus.
                Histogram Based
• Histogram based
   – In a cricket video, we usually get two kinds of frames, one
     with field where there are very narrow range of colors
     present and of crowd where many different kinds of colors
     are present.
   – The histogram of crowd will be flat and will cover the
     whole range, whereas other scenes will have histograms
     which will be concentrated in a narrow range.
• Results and Inferences
   – The results were not that encouraging. A single band or
     combination of bands, and also the range of values within
     a histogram could not be determined for histogram to be
     constructed and analyzed.
         Edge Based Detection
• Edge Based
  – We observed that energy profile of the frame
    containing crowd is distinctly higher than those of
    not containing crowd, so we used canny edge
    detector and build a edge map of all the frames,
    now comparing the energy profiles of the edge
    map solved our problem
• Results and Inferences
  – The results were very good
             Pitch Detection
• Finding whether the frame is showing a pitch
  or not and determining its position in the
         Template Based solution
• Template Matching
   – We took a narrow horizontal strip, from the middle of a
     frame showing the pitch, as the template. For each frame
     in the video, we looked whether the strip can match
     somewhere near the middle of the frame. We used
     normalized correlation and square difference error as the
     matching standards.
• Results and Inferences
   – The results were not that good, had few false positives in
     the frame showing the field.
   – The problem with template matching was that it did not
     address the variation of color of the pitch under different
     lighting conditions.
                   HSI space based
• As template matching didn’t look into the variation of lighting
• The idea is that any frame not having the pitch will not be able to
  match in all the planes, viz. Hue, Saturation and Intensity, even if we
  take sufficiently large margins so that all the frames showing the
  pitch are correctly classified
• For each frame, we broke it into HSI space and applied thresholding
  on all the planes according to the ranges calculated before.
• We dilated each of the planes for filling the small holes and then
  took the intersection of the resulting planes.
• We again dilated and eroded the resulting plane to remove the
• After that we took the distance transform of the image and
  searched for the maximum value in that. Thresholding this
  maximum value worked as the blob detection
• The results were very good

         Intensity             Hue    Saturation

        Intersection           Blob    Original
        Bowling Side Detection
• To detect from which side the bowler bowled.
• Solution
  – Erosion of the blob obtained above shows the
    skeleton of the pitch which is available.
  – Whenever a bowler bowls the skeleton becomes
    “L” shaped due to the occlusion caused by the
  – By detecting the orientation of “L” we can easily
    determine the side from which the ball was being
• This method gave very good results

            Hue              Saturation   Intensity

         Intersection        Skeleton      Original
• Later in the video as the part of the pitch was
  covered with shadows, the “L” shape got
  truncated and it was hard to determine the
  bowling side.

           Skeleton                   Original

                      Truncated “L”      Bowling side not
                                        detected, but pitch
                                         was still detected
      Ball by Ball Segmentation
• To segment the video into deliveries
• Solution
  – The detection of “L” can also help in knowing
    when the ball is being bowled. When the skeleton
    of the pitch changes from rectangle to L shaped
    then a ball was bowled. We also made sure that
    there was a gap of a few seconds between the
    deliveries by setting a timer.
               Classified as a

          This was not classified as a
           delivery due to the timer

                Again Classified as
                delivery, as timer
                    got reset
                Summary Making
• To generate a meaningful summary out of the video.
• Solution
   – By a simple assumption that crowd is displayed only when
     there is six, four, wicket or sometimes ads.
   – So, every time crowd is detected we can go back few
     seconds and take that part as highlight.
   – Now sometimes crowd is displayed after an ad break, so
     now pitch detection helps here, every time we go back we
     can check for the presence of pitch, if it is detected then it
     is not from the ad break and we can include that part in
     our summary and if it is not we can skip that.

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