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. Temporally Unclipped is a real-world representation of videos. Action recognition needs to be adapted for it.

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

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

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

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.

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

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

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