While data augmentation serves as a potent strategy to encompass corner cases in autonomous driving, scant research has attended to the imperative of maintaining categorical balance post-augmentation. Hereby, We introduce a novel data augmentation technique designed to proficiently regulate the fine-grained distribution of pedestrian poses within datasets, thereby amplifying the efficacy of pedestrian recognition algorithms in traffic situations. Our approach is founded on a unique quadratic categorization of human pose situations, using corresponding pose estimation confidence scores. This methodology strives to precisely adjust the proportion of poses within differing confidence score ranges. Experimental outcomes demonstrate that our approach not only enhances the distribution of pedestrian poses in traffic image datasets, but also significantly boosts the performance of pedestrian detectors in final.