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A comprehensive survey on human pose estimation approaches

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Abstract

The human pose estimation is a significant issue that has been taken into consideration in the computer vision network for recent decades. It is a vital advance toward understanding individuals in videos and still images. In simple terms, a human pose estimation model takes in an image or video and estimates the position of a person’s skeletal joints in either 2D or 3D space. Several studies on human posture estimation can be found in the literature, however, they center around a specific class; for instance, model-based methodologies or human movement investigation, and so on. Later, various Deep Learning (DL) algorithms came into existence to overcome the difficulties which were there in the earlier approaches. In this study, an exhaustive review of human pose estimation (HPE), including milestone work and recent advancements is carried out. This survey discusses the different two-dimensional (2D) and three-dimensional human (3D) pose estimation techniques along with their classical and deep learning approaches which provide the solution to the various computer vision problems. Moreover, the paper also considers the different deep learning models used in pose estimation, and the analysis of 2D and 3D datasets is done. Some of the evaluation metrics used for estimating human poses are also discussed here. By knowing the direction of the individuals, HPE opens a road for a few real-life applications some of which are talked about in this study.

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    Shradha Dubey & Manish Dixit

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Dubey, S., Dixit, M. A comprehensive survey on human pose estimation approaches. Multimedia Systems 29, 167–195 (2023). https://doi.org/10.1007/s00530-022-00980-0

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