生物识别
步态
计算机科学
人工智能
领域(数学)
深度学习
鉴定(生物学)
机器学习
步态分析
物理医学与康复
医学
植物
数学
纯数学
生物
作者
Akash Pundir,Manmohan Sharma
标识
DOI:10.1109/inocon57975.2023.10101267
摘要
Human gait identification is an expanding field with potential use-cases in fields such as security, human-machine communication, and biometrics. In last few years, deep learning-based system have become the leading method in gait identification. This research presents a summary of the most recent developments in deep learning-based human gait recognition models. We examine the various architectures, characteristics, evaluation metrics, and datasets employed in these models, as well as discussing the challenges and future possibilities in this field. These include the requirement for more extensive and diverse datasets, the integration of multimodal information, and the construction of models that can withstand variations in gait. Our research offers a comprehensive understanding of the current state of human gait recognition models and the direction for future research.
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