k-fold Subsampling based Sequential Backward Feature Elimination
To address the redundancy and insufficient discriminability of shape features in human detection, this paper proposes a novel feature selection method integrating filter- and wrapper-based strategies. We innovatively design a k-fold subsampling-driven Sequential Backward Elimination (SBE) framework that simultaneously optimizes feature robustness and discriminability while preserving essential shape representation capability, thereby significantly reducing feature dimensionality. Coupled with a linear SVM classifier, our method is evaluated on the INRIA and ETH pedestrian datasets. Results show that it achieves over 50% faster detection speed and a 2% improvement in mean Average Precision (mAP) compared to the current state-of-the-art approaches; relative to the Deformable Parts Model (DPM), it yields approximately a 9% mAP gain. The proposed framework establishes an interpretable, reusable paradigm for lightweight and efficient human detection through principled feature selection.