  Convolutional Architecture for Fast Feature Encoding (CAFFE) [11] is a
software package for the training, classifying, and feature extraction of
images. The UCF Sports Action dataset is a widely used machine learning dataset
that has 200 videos taken in 720x480 resolution of 9 different sporting
activities: diving, golf, swinging, kicking, lifting, horseback riding,
running, skateboarding, swinging (various gymnastics), and walking. In this
report we report on a caffe feature extraction pipeline of images taken from
the videos of the UCF Sports Action dataset. A similar test was performed on
overfeat, and results were inferior to caffe. This study is intended to explore
the architecture and hyper parameters needed for effective static analysis of
action in videos and classification over a variety of image datasets.
