Levi Smith. Reading papers Getting data set together Clipping videos to form the training and...

REPORT 2Levi Smith

PROJECT STATUS

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

PAPERS

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

PAPERS

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

PAPERS

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 keyframe or from a set of frames

PROJECT

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

PROJECT OVERVIEW

Train classifier to detect shot boundary and classification

Localize the shot on the field Assign probabilities for each action to

locations on the field

SHOT DETECTION

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 Box Long shot

SHOT LOCALIZATION

Take a shot and localize it on the field by matching features

Field symmetry could present a challenge

ASSIGN PROBABILITIES

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 .8Foul .3

………

Goal .10Foul .5

………

Corner .8