Hybrid Reconstruction Approaches for Occluded Regions in Multi-Person Gait Recognition

This work introduces a hybrid two-stage framework for reconstructing occluded body landmarks in multi-person gait recognition, addressing a major barrier to deploying gait biometrics in real-world surveillance. Unlike prior methods that use either numerical interpolation or optimization alone, the proposed approach fuses cubic spline interpolation with optimization-based refinement (ANN, ALS, and PSO-NN), where interpolation establishes a biomechanically consistent baseline that optimization then refines. Evaluated on the benchmark SMVDU dataset and a newly collected RUET-MG dataset across controlled occlusion levels of 10–40%, the PSO-NN variant consistently achieves the lowest reconstruction error (X-MSE of 0.103 on SMVDU-MG). This represents an improvement of over 97% against optimization-only methods and over 75% against interpolation-only methods, establishing the fused pipeline as a practical, occlusion-robust solution for outdoor multi-person gait analysis.