A Literature Review: Geometric Methods and Their Applications in Human-Related Analysis

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 12; p. 2809
• Main Authors: Gong, Wenjuan, Zhang, Bin, Wang, Chaoqi, Yue, Hanbing, Li, Chuantao, Xing, Linjie, Qiao, Yu, Zhang, Weishan, Gong, Faming
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.06.2019; MDPI
• Subjects: Artificial Intelligence; Computer Graphics; Computer vision; Deep Learning; geometric methods; human action recognition; human body shape analysis; human pose estimation; Humans; Literature reviews; Nonlinear equations; Review; Topological manifolds; human action recognition; human pose estimation; geometric methods; human body shape analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19122809

Geometric features, such as the topological and manifold properties, are utilized to extract geometric properties. Geometric methods that exploit the applications of geometrics, e.g., geometric features, are widely used in computer graphics and computer vision problems. This review presents a literature review on geometric concepts, geometric methods, and their applications in human-related analysis, e.g., human shape analysis, human pose analysis, and human action analysis. This review proposes to categorize geometric methods based on the scope of the geometric properties that are extracted: object-oriented geometric methods, feature-oriented geometric methods, and routine-based geometric methods. Considering the broad applications of deep learning methods, this review also studies geometric deep learning, which has recently become a popular topic of research. Validation datasets are collected, and method performances are collected and compared. Finally, research trends and possible research topics are discussed.