Convolutional Architecture Exploration for Action Recognition and Image Classification

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2015
Accession Number
ADA624847

Entities

People

  • David Aha
  • J. T. Turner
  • Kalyan M. Gupta
  • Leslie Smith

Organizations

  • Knexus Research (United States)

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Computer Programs
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Data Sets
  • Dimensionality Reduction
  • Feature Extraction
  • Graphics Processing Unit
  • Learning
  • Machine Learning
  • Neural Networks
  • Recognition
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Educational Psychology
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks