Hierarchical Causal Decoupling Pose Estimation Model for Special Operations Behavior Monitoring

초록

Adherence to standardized poses is paramount for ensuring safety in special operations, where non-standard poses-often resulting from negligence or inexperience-can precipitate personal injury and property damage. Human Pose Estimation (HPE) presents an avenue for the automated evaluation of behavior pose standardization. However, monitoring special operations necessitates exceptionally high HPE accuracy under stringent thresholds (e.g., Object Keypoint Similarity with threshold >0.75) for reliable pose reconstruction to ensure safety. Complex environments introduce numerous non-causal confounding factors into feature representations, reducing the performance of existing HPE models, which are primarily reliant on feature correlations. To address this, Hierarchical Causal Decoupling Pose Estimation (HCDPE) model is proposed. Specifically, HCDPE decouples image features into causal and non-causal features. Meanwhile, by integrating multi-hypothesis structure and graph attention, a hierarchical causal gating module is proposed. This enables HCDPE to intervene in causal features at multiple granularities, thereby finely indexing causal correlations between features and pose representation. To optimize the above causal decoupling, the counterfactual causal effect estimation module is designed. This method guides the generation of loss by minimizing the mutual information between causal and non-causal features, thereby achieving causal effect estimation. Together, these components form a closed-loop causal inference chain, enabling HCDPE to achieve fine-grained HPE. Extensive experiments on a specialized dataset for special operations, and two public benchmarks, demonstrate the superior performance of HCDPE in engineering applications. The code is publicly available at https://github.com/onepionts/HCDPE Note to Practitioners-This paper explores the application of human pose estimation methods to industrial special operations. This is used to monitor workers behavior poses and ensure their safety. However, due to interference from irrelevant factors such as occlusion or background noise, existing human pose estimation methods often struggle to accurately recognize human pose in complex environments. This makes them unable to meet the requirements of safety monitoring for special operations. Our solution, the Hierarchical Causal Decoupling Pose Estimation

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