计算机科学
自编码
人工智能
代表(政治)
分割
注释
模式识别(心理学)
骨架(计算机编程)
序列(生物学)
相似性(几何)
特征学习
编码(集合论)
深度学习
集合(抽象数据类型)
图像(数学)
政治
生物
政治学
法学
遗传学
程序设计语言
作者
Jan Sedmidubský,Fabio Carrara,Giuseppe Amato
标识
DOI:10.1007/978-3-031-28238-6_8
摘要
Recent progress in pose-estimation methods enables the extraction of sufficiently-precise 3D human skeleton data from ordinary videos, which offers great opportunities for a wide range of applications. However, such spatio-temporal data are typically extracted in the form of a continuous skeleton sequence without any information about semantic segmentation or annotation. To make the extracted data reusable for further processing, there is a need to access them based on their content. In this paper, we introduce a universal retrieval approach that compares any two skeleton sequences based on temporal order and similarities of their underlying segments. The similarity of segments is determined by their content-preserving low-dimensional code representation that is learned using the Variational AutoEncoder principle in an unsupervised way. The quality of the proposed representation is validated in retrieval and classification scenarios; our proposal outperforms the state-of-the-art approaches in effectiveness and reaches speed-ups up to 64x on common skeleton sequence datasets.
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