Abstract
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.
Highlights
With the emergence of low-cost RGB-D cameras, human bodies can be digitized at a lower cost [1,2,3], and their actions can be captured [4,5]
Constructed graphs are decomposed into substructures called subgraphs, and these subgraphs are compared based on a proposed graph kernel named the subgraph-pattern graph kernel (SPGK)
The original method was proposed under the bag of words (BoW) pipeline, but theoretically, it can be adapted to the deep learning architecture
Summary
With the emergence of low-cost RGB-D cameras, human bodies can be digitized at a lower cost [1,2,3], and their actions can be captured [4,5]. Some methods align the data before using a Euclidean metric, e.g., through dynamic time warping (DTW) [13], specialized kernels or a Fourier hierarchical pyramid [14]; other methods transform the data before using them, e.g., covariance features [15] None of these methods consider the implicit dynamics of the sequences and the lower dimensional space where the features lie. Attributes and theories in non-Euclidean geometric spaces are explored These geometric methods and their applications in human-related analysis are collected and studied. Geometric methods and their applications in human-related analysis are extensively studied.
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