Exploring Gait Biometrics within the domain of deep learning offers a potent fusion that significantly enhances pattern recognition capabilities. Over the past decade, the evolution of deep learning (DL) pipelines has showcased their effectiveness in overcoming complex challenges within image and signal processing applications. Constructing these pipelines requires a deep understanding of the diverse intermediate layers and their implications. The iterative refinement process involves careful selection and rigorous performance validation of each configuration, demanding significant time and contemplation. Consequently, the task of selecting a robust DL pipeline that excels across various datasets remains challenging. The central objective of this review is to provide guidance to researchers, fostering a comprehensive grasp of distinct gait sensing technologies, while establishing a solid foundation in deep learning concepts. Although gait recognition is a relatively recent development and is yet to find widespread application in real-world scenarios, this article offers a thorough examination of gait biometrics tailored specifically for real-time surveillance applications. Delving into the complexities, it elucidates the crucial parameters governing deep learning pipelines and their nuanced selection to address specific challenges. Through an analysis of recent research articles on deep learning models and their performance across diverse datasets, the review outlines the merits and demerits of various approaches. The ultimate aim is to facilitate the development of an optimized pipeline that seamlessly integrates existing methodologies, enabling the attainment of swift yet precise results for a given problem.

中文翻译：

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步态生物识别之旅：集成深度学习以增强模式识别

在深度学习领域探索步态生物识别技术提供了一种有效的融合，可以显着增强模式识别能力。在过去的十年中，深度学习 (DL) 管道的发展展示了其在克服图像和信号处理应用中的复杂挑战方面的有效性。构建这些管道需要深入了解不同的中间层及其含义。迭代细化过程涉及对每个配置的仔细选择和严格的性能验证，需要大量的时间和思考。因此，选择一个在各种数据集上表现出色的强大的深度学习管道仍然具有挑战性。本次综述的中心目标是为研究人员提供指导，促进对不同步态传感技术的全面掌握，同时为深度学习概念奠定坚实的基础。尽管步态识别是一个相对较新的发展，尚未在现实​​场景中得到广泛应用，但本文对专为实时监控应用定制的步态生物识别技术进行了彻底的检查。它深入研究了复杂性，阐明了控制深度学习管道的关键参数及其微妙的选择，以应对特定的挑战。通过对最近关于深度学习模型及其在不同数据集上的表现的研究文章的分析，该评论概述了各种方法的优点和缺点。最终目标是促进开发无缝集成现有方法的优化流程，从而针对给定问题获得快速而精确的结果。