Information Extraction from Cricket Videos

Syed Ahsan IshtiaqueKumar 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 frames

– 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

Every frame here is a hard transition, but the Histogram approach will not be able to detect this

Hard Transition

Hard Transition

Hard Transition

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 transition.

– Also blurring of the frames improved correlation and hence the results

• 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 problem.

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 frame.

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

conditions• 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 holes.

• 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

Results• The results were very good

Intensity SaturationHue

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 bowler.

– By detecting the orientation of “L” we can easily determine the side from which the ball was being bowled

Results • This method gave very good results

Hue

Intersection

Intensity

Skeleton Original

Saturation

Difficulties• 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.

Results

This was not classified as a delivery due to the timer

Classified as a delivery

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.