Baseline results for the challenge problem of HumanID using gait analysis

Identification of people from gait captured on video has become a challenge problem in computer vision. However there is not a baseline algorithm or standard dataset for measuring, or determining what factors affect performance. In fact, the conditions under which the problem is "solvable" are not understood or characterized. This paper describes a large set of video sequences (about 300 GB of data related to 452 sequences from 74 subjects) acquired to investigate important dimensions of this problem, such as variations due to viewpoint, footwear, and walking surface. We introduce the HumanID challenge problem. The challenge problem contains a set of experiments of increasing difficulty, a baseline algorithm, and its performance on the challenge problem. Our results suggest that differences in footwear or walking surface type between the gallery and probe video sequence are factors that affect performance. The data set, the source code for the baseline algorithm, and UNIX scripts to reproduce the basic results reported are available to the research community at http://marathon.csee.usf.edu/GaitBaseline/