Convolutional Architecture Exploration for Action Recognition and Image Classification

reportActive / Technical Report | Accession Number: ADA624847 | Open PDF

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.

Author(s):
Turner, J. T. ; Aha, David ; Smith, Leslie ; Gupta, Kalyan M.
Author Organization(s):
KNEXUS RESEARCH CORP NATIONAL HARBOR MD
Pagination:
0013

Security Markings

DOCUMENT & CONTEXTUAL SUMMARY

Distribution:
Approved For Public Release
Distribution Statement:
Approved For Public Release; Distribution Is Unlimited.

RECORD

Collection: TR
Identifying Numbers
Monitor Series:
NCARAI
 Subject Terms
Joint Capability Areas:
JCA_1.2.1_Training; JCA_1.2.7_Experimentation; JCA_5_Command and Control
Modernization Areas:
AI and Machine Learning
Communities of Interest:
Energy and Power Technologies
Descriptor(s):
*FEATURE EXTRACTION, *IMAGE PROCESSING, *LEARNING MACHINES, COMPUTER VISION, MOVING TARGETS, MULTIPLE TARGETS, NETWORK ARCHITECTURE, PATTERN RECOGNITION, RELATIONAL DATA BASES, SOFTWARE TOOLS, STATIC TESTS, TARGET CLASSIFICATION, TARGET DETECTION, TRAINING, VIDEO SIGNALS
Field(s)/Group(s):
Computer Programming and Software, Cybernetics
Keyword(s):
IMAGE DATASETS, CAFFE(CONVOLUTIONAL ARCHITECTURE FOR FAST FEATURE ENCODING)
Report Date:
2015 Jan 01
Creation Date:
2015 Dec 30