1. Levi Smith

2.  Reading papers  Getting data set together  Clipping videos to form the training and testing data for our classifier  Project separation  Christian will focus on action detection and recognition  My focus will be on shot type detection and localization on the field

3.  CRAM: Compact Representation of Actions in Movies  Display concurrently the desired portions of the video  Extracts actions of interest from 3D optical flow field  Use action template to find similar actions within the given video  Not good for group actions, such as those on the soccer field  We do not want to display all of our events concurrently, but some of the techniques could prove useful for action detection

4.  An Effective Soccer Video Shot Detection Algorithm  Only uses the frame color histogram to categorize shots  Looks at amount of green pixels to verify if field is visible or not  Would be advantageous to look at more features to detect and classify shots

5.  Automatic Soccer Video Analysis and Summarization  Shot boundary detection  Absolute difference between two frames in their ratios of dominant (grass) colored pixels to total number of pixels  Difference in color histogram similarity  Shot classification  Utilize a Golden section composition rule, where they look at the amount of grass colored pixels in each region of the subdivided frame  Shot class can be determined from single key frame or from a set of frames

6.  Goal  Extract a meaningful summary of the sports video provided  Method  Combine action recognition and shot detection/classification techniques  Assign probabilities to field locations for each localized action to assist in action classification

7.  Train classifier to detect shot boundary and classification  Localize the shot on the field  Assign probabilities for each action to locations on the field

8.  Train a classifier, which will give us confidence levels  Given a shot, classify it as one of a list of types  Panoramic, audience, zoomed in, corner, goal post, penalty box  Features  CSIFT  STIP  HOG Penalty BoxLong shot

9.  Take a shot and localize it on the field by matching features  Field symmetry could present a challenge

10.  For each location on the field, assign a probability to each action to assist with classification  When given a new shot to classify, we will use this probability to increase our confidence in the action detection Goal.8 Foul.3 … Goal.10 Foul.5 … Corner.8