Does My Gait Look Nice? Human Perception-Based Gait Relative Attribute Estimation Using Dense Trajectory Analysis

Relative attributes play an important role in object recognition and image classification tasks. These attributes provide high-level semantic explanations for describing and relating objects to each other instead of using direct labels for each object. In the current study, we propose a new method utilizing relative attribute estimation for gait recognition. First, we propose a robust gait motion representation system based on extracted dense trajectories (DTs) from video footage of gait, which is more suitable for gait attribute estimation than existing heavily body shape-dependent appearance-based features, such as gait energy images (GEI). Specifically, we used a Fisher vector (FV) encoding framework and histograms of optical flows (HOFs) computed with individual DTs. We then compiled a novel gait dataset containing 1,200 videos of walking subjects and annotation of gait relative attributes based on subjective perception of gait pairs of subjects. To estimate relative attributes, we trained a set of ranking functions for the relative attributes using a Rank-SVM classifier method. These ranking functions estimated a score indicating the strength of the presence of each attribute for each walking subject. The experimental results revealed that the proposed method was able to represent gait attributes well, and that the proposed gait motion descriptor achieved better generalization performance than GEI for gait attribute estimation.