1 / 10

Report 2

Levi Smith. Report 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.

twyla
Download Presentation

Report 2

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Levi Smith Report 2

  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 Project Status

  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 Papers

  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 Papers

  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 Papers

  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 Project

  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 Project Overview

  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 Shot Detection Penalty Box Long shot

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

  10. 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 Corner .8 Goal .8 Foul .3 … … … Goal .10 Foul .5 … … …

More Related