Performance Evaluation of Model-Based Gait on Multi-View Very Large Population Database With Pose Sequences

Abstract

Model-based gait recognition is considered to be promising due to the robustness against some variations, such as clothing and baggage carried. Although model-based gait recognition has not been fully explored due to the difficulty of human body model fitting and the lack of a large-scale gait database, recent progress in deep learning-based approaches to human body model fitting and human pose estimation is mitigating the difficulty. In this paper, we, therefore, address the remaining issue by presenting a large-scale human pose-based gait database, OUMVLP-Pose, which is based on a publicly available multi-view large-scale gait database, OUMVLP. OUMVLP-Pose has many unique advantages compared with other public databases. First, OUMVLP-Pose is the first gait database that provides two datasets of human pose sequences extracted by two standard deep learning-based pose estimation algorithms, OpenPose and AlphaPose. Second, it contains multi-view large-scale data, i.e., over 10,000 subjects and 14 views for each subject. In addition, we also provide benchmarks in which different kinds of gait recognition methods, including model-based methods and appearance-based methods, have been evaluated comprehensively. The model-based gait recognition methods have shown promising performances. We believe this database, OUMVLP-Pose, will greatly promote model-based gait recognition in the next few years.