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.

翻译：快速地貌编码革命架构(CAFFE) [11] 是用于培训、分类和特别提取图像的软件包(CAFFFE) [11] 。UCF体育行动数据集是一个广泛使用的机器学习数据集,在720x480分辨率中共拍摄了200个视频,涉及9种不同的体育活动:潜水、高尔夫、摇摆、踢脚、举起、骑马、跑步、滑板滑板、摇摆(各种体操)和步行。我们在本报告中报告了从UCF体育行动数据集视频中提取图像的咖啡因特征管道。在超额座位上进行了类似的测试,结果优于Caffe。这项研究旨在探索对各种图像数据集的视频和分类行动进行有效静态分析所需的架构和超高参数。