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Published byYanti Iskandar Modified over 6 years ago
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Action Recognition in Temporally Untrimmed Videos
Fatemeh Yazdiananari
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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.
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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
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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
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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
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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.
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Ground truth of all data both test and training data
SVM demos Ground truth of all data both test and training data
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SVM demos Small unfilled circles are the trained data, filled circles are the tested data. Only were classified as positive.
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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
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