FeEval A Dataset for Evaluation of Spatio-temporal Local Features

The most successful approaches to video understanding and video matching use local spatio-temporal features as a sparse representation for video content. Until now, no principled evaluation of these features has been done. We present FeEval, a dataset for the evaluation of such features. For the first time, this dataset allows for a systematic measurement of the stability and the invariance of local features in videos. FeEval consists of 30 original videos from a great variety of different sources, including HDTV shows, 1080p HD movies and surveillance cameras. The videos are iteratively varied by increasing blur, noise, increasing or decreasing light, median filter, compression quality, scale and rotation leading to a total of 1710 video clips. Homography matrices are provided for geometric transformations. The surveillance videos are taken from 4 different angles in a calibrated environment. Similar to prior work on 2D images, this leads to a repeatability and matching measurement in videos for spatio-temporal features estimating the overlap of features under increasing changes in the data.