To address challenges including severe occlusion, large-scale variation, inaccurate keypoint localization, and strict real-time constraints in subway surveillance scenarios, this paper proposes a lightweight human pose estimation framework based on an enhanced YOLOv8-Pose architecture. The proposed method introduces four key components: a DWR (Dilation-wise Residual) module to expand multi-scale receptive fields for improved joint context modeling, an HFERB (High-Frequency Enhancement Residual Block) to recover fine-grained structural details lost during downsampling, an OrthoNets-based orthogonal attention mechanism to reduce channel redundancy while preserving discriminative representations, and an MFEB (Multi-Scale Feature Enhancement Block) to enable adaptive feature fusion under varying crowd densities. Extensive experiments on the COCO 2017 dataset demonstrate that the proposed method achieves 87.9% mAP@0.5 and 64.9% mAP@0.5–0.95, improving the baseline YOLOv8-Pose by 1.7% and 1.0%, respectively, while reducing validation pose loss by 5.77%. After lightweight optimization with ONNX export and FP16 inference, the model is compressed to 3.99M parameters and achieves 76 FPS inference speed on the same hardware, significantly improving efficiency compared with the baseline, while achieving 38 FPS on the full model. Both configurations satisfy real-time constraints for subway surveillance deployment. Qualitative results on real subway surveillance videos further demonstrate strong robustness under occlusion and scale variation. Overall, the proposed method achieves a favorable trade-off between accuracy and efficiency, making it suitable for real-time human pose estimation in intelligent transportation systems.

Aiming to address the problems of scale uncertainty, feature extraction difficulty, model training difficulty, poor real-time performance, and sample imbalance in low-resolution images for gender recognition, this study proposes an efficient pedestrian gender recognition model based on key area feature extraction and fusion. First, a discrete cosine transform (DCT)-based local super-resolution preprocessing algorithm is developed for facial image gender recognition. Then, a key area feature extraction and information fusion model is designed, using additional appearance features to assist in gender recognition and improve accuracy. The proposed model preprocesses images using the DCT image fusion and super-resolution methods, dividing pedestrian images into three regions: face, hair, and lower body (legs) regions. Features are separately extracted from each of the three image regions. Finally, a multi-region local gender recognition classifier is designed and trained, employing decision-level information fusion. The results of the three local classifiers are fused using a Bayesian computation-based fusion strategy to obtain the final recognition result of a pedestrian’s gender. This study uses surveillance video data to create a dataset for experimental comparison. Experimental results demonstrate the superiority of the proposed approach. The facial model (DCT-PFSR-CNN) achieved the best accuracy of 89% and an F1-Score of 0.88. Furthermore, the complete pedestrian model (MPGRM) attained an mAP of 0.85 and an AUC of 0.86, surpassing the strongest baseline (HDFL) by 2.4% in mAP and 2.3% in AUC. These results confirm the high application potential of the proposed method for gender recognition in real-world surveillance scenarios.