Detection of Climbing Rod Motion Changes Based on 3D Pose Estimation
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Abstract
3D human pose estimation has important engineering application value in industrial scenarios such as safety monitoring of power grid pole-climbing operations and standardized action training. Existing skeleton-based human behavior recognition methods still have notable limitations in modeling long-term temporal dependencies, capturing multi-scale dynamic motion patterns, and resolving geometric ambiguities in 2D-to-3D pose lifting. This paper proposes a graph-based network integrating multi-scale temporal convolution with spatial attention, and an interactive spatio-temporal Transformer, and develops an end-to-end web-based recognition system. Experiments on public benchmark datasets fully verify the performance of the proposed methods, which can provide technical support for skeleton-based action recognition and deployment of intelligent perception systems in industrial scenarios.
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