Convolutional Architecture Exploration for Action Recognition and Image Classification
KNEXUS RESEARCH CORP NATIONAL HARBOR MD
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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.
- Computer Programming and Software