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. Ground truth of all data both test and training data
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