1 / 9

Action Recognition in Temporally Untrimmed Videos

Action Recognition in Temporally Untrimmed Videos. Fatemeh Yazdiananari. Temporally Clipped v.s Unclipped. Temporally Clipped: Videos only contain the action. Temporally Unclipped: Videos contain both the action and non-action.

lonato
Download Presentation

Action Recognition in Temporally Untrimmed Videos

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. Action Recognition in Temporally Untrimmed Videos • Fatemeh Yazdiananari

  2. Temporally Clipped v.s Unclipped • Temporally Clipped: Videos only contain the action. • Temporally Unclipped: Videos contain both the action and non-action. • Temporally Unclipped is a real-world representation of videos. Action recognition needs to be adapted for it.

  3. Unclipped Videos • Contains more then the action • Determine the temporal location and the action itself • Make temporally clipped recognition methods suitable for unclipped data • We are considering 4 different methods

  4. The 4 Methods • 1. Dividing a video into clips • 2. Overlapping Sliding Windows in time • 3. Spatiotemporal Segmentation • 4. Graphical Model: Capturing the relationship of clips

  5. Baseline Action Recognition • Using DTF features • HOG, HOF, MBH, Trajectory • Bag of Words model • Feature Vector: each video is represented by a histogram of visual words • SVM is used as the classifier

  6. Preliminary Steps • Download UCF101, DTF, three split files • Run and understand demos of SVM • Work on UCF101 baseline • Write code to load Features, Labels, and Names of each video.

  7. SVM demos Ground truth of all data both test and training data

  8. SVM demos Small unfilled circles are the trained data, filled circles are the tested data. Only were classified as positive.

  9. Code • Feature matrix (DTF) : (13320, 16000) • Label Vector : (13320,1) • Name Vector : (13320,1) • Next step is to optimize this into a structure for each video with feature, label, name and index • Optimization will help me run a comparison with the Train/Test splits and implement MultiClass SVM • Next week I will be able to run baseline and get accuracy percentage of UCF101

More Related