1. Data Driven Attributes for Action Detection
Week 5
Presented by Christina Peterson

2. Bag of Words Implemented the Bag of Words in Matlab
For each video, collect low level features within the ground truth bounding box
At each frame, the box is divided into 3 x 3 cells
For each cell, create a histogram for each feature (STIP, color, texture)
For each bounding box, create a histogram for each feature
Feature Vector is the concatenation of the histograms
One Feature Vector for every bounding box

3. Exemplar-SVM
Obtained source code from Tomasz Malisiewicz’s website for ‘Ensemble of Exemplar SVMS for Object Detection and Beyond’
Do implementation based on this code, but designed for the UCF Sports dataset
For each action class, create Exemplar-SVM for each bounding box of every video
Optimize by reducing bounding box to one cycle of the action

4. Calibration Run each Exemplar-SVM on a validation set
Use non-maximum suppression to remove redundant detections
Compute the overlap score between resulting detections and ground-truth bounding-boxes
Detections that overlap by more than 0.5 with ground truth are positive
Detections with overlap by less than 0.2 with ground truth are negative
Fit a logistic function to these scores

5. Goals Implement the Exemplar-SVMs
Implement the calibration step as detailed in Malisiewicz et al. [1]
Research other calibration methods to improve this step

6. References
[1] T. Malisiewicz, A. Gupta, and A. A. Efros. Ensemble of Exemplar SVMS for Object Detection and Beyond. In Proc. ICCV, [2] Y. Tian, R. Sukthankar, and M. Shah. Spatiotemporal Deformable Part Models for Action Detection. In CVPR, 2013.