The gait identification challenge problem: data sets and baseline algorithm

Abstract

Recognition of people through gait analysis is an important research topic, with potential applications in video surveillance, tracking, and monitoring. Recognizing the importance of evaluating and comparing possible competing solutions to this problem, we previously introduced the HumanID challenge problem consisting of a set of experiments of increasing difficulty, a baseline algorithm, and a large set of video sequences (about 300 GB of data related to 452 sequences from 74 subjects) acquired to investigate important dimensions to this problem, such as variations due to viewpoint, footwear and walking surface. In this paper we present a detailed investigation of the baseline algorithm, quantify the dependence of the various covariates on gait-based identification, and update the previous baseline performance with optimized ones. We establish that the performance of the baseline algorithm is robust with respect to its various parameters. The overall identification performance is also stable with respect to the quality of the silhouettes. We find that the approximately lower 20% of the silhouette accounts for most of the recognition achieved. Viewpoint has barely statistically significant effect on identification rates, whereas footwear and surface-type does have significant effects with the effect due to surface-type being approximately 5 times that of shoe-type.