In industrial environments, robust Temporal Action Localization (TAL) is essential; however, frequent occlusions often compromise the reliability of skeletal data, leading to negative transfer in multimodal fusion. To address this challenge, we propose a Gated Skeleton Refinement Module (Gated SRM), a universal front-end preprocessing module that explicitly incorporates OpenPose confidence scores into the network architecture. By applying these scores as a logarithmic bias within a self-attention mechanism, our method achieves soft suppression—dynamically attenuating the attention weights assigned to unreliable joints—before adaptively fusing the refined skeletal features with RGB representations through a learnable gating network. Extensive experiments on the heavily occluded IKEA ASM dataset demonstrate that our approach effectively prevents the catastrophic accuracy degradation typical of naive and established multimodal fusion strategies, improving the mean Average Precision (mAP) to 21.77%, maintaining parity with the RGB-only baseline while demonstrating superior robustness. Furthermore, the system maintains a practical end-to-end inference speed of approximately 9.2 frames per second (FPS), which is sufficient for monitoring macro-level industrial workflows. By prioritizing confidence-based data selection over data restoration, this sensor-metadata-driven architecture offers a robust and principled approach acting as a critical fail-safe and safety-net for real-world action recognition under occlusion.
Takami et al. (Thu,) studied this question.