Deterministic Learning for Human Gait Recognition

被引:0
|
作者
Yang, Feifei [1 ]
Si, Wenjie [2 ]
Zeng, Wei [3 ]
Wang, Qian [4 ]
机构
[1] South China Univ Technol, Sch Mat Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China
[2] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China
[3] Longyan Univ, Sch Mech Elect Engn, Longyan 364012, Peoples R China
[4] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China
关键词
Deterministic Learning; Temporal Data Sequence; Joint Angle; Human Gait Recognition; Similarity Definition; WALKING;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we investigate the problem of human gait recognition based on temporal data sequences by utilizing deterministic learning theory. We employ joint-angle trajectories of lower limbs for gait recognition. The joint angle data generated from human gait locomotion is chosen as the temporal data since it represents the dynamic characteristics of human gait and contains lots of valuable information for gait recognition. What's more, the joint angle trajectories are periodic or periodic-like (recurrent) which makes the radial basis function (RBF) network easily satisfy the partial persistence of excitation (PE) condition. Firstly, discrete-time joint angle data obtained by motion-capture equipment or image-processing algorithms forms the temporal data sequences generated from human gait locomotion, locally-accurate approximation of the underlying gait system dynamics is achieved by using RBF networks. We then prove the convergence of the approximation error and related parameters. Consequently, the joint angle data sequences can effectively represent human gait locomotion by using the knowledge of approximated gait dynamics which is kept in constant RBF networks. Finally, similarity definition for temporal gait data sequences generated from different persons or from different status of one person is given, and a method for recognition of gait temporal data sequences is proposed. We use less complicated simulation examples of compass-like biped robots gait recognition to demonstrate the effectiveness of our schemes.
引用
收藏
页码:3045 / 3050
页数:6
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