This paper proposes a novel method to construct a versatile pedestrian knowledge bank for robust pedestrian detection. The method extracts generalized pedestrian knowledge from a large-scale pretrained model, such as CLIP, and curates them by quantizing the most representative features and guiding them to be distinguishable from background scenes. The knowledge bank is then used to complement and enhance pedestrian features within a pedestrian detection framework. Through comprehensive experiments, the method demonstrates its effectiveness and outperforms state-of-the-art detection performances on four public pedestrian detection datasets: CrowdHuman, WiderPedestrian, CityPersons, and Caltech. The key contributions include proposing a novel method to acquire versatile pedestrian representations applicable to diverse frameworks and scenes, constructing a versatile pedestrian knowledge bank by storing generalized and task-compatible knowledge, and verifying the effectiveness and versatility of the method with various detection frameworks and diverse datasets. The method is applicable to various detection frameworks, including region proposal based two stage detection and query based detection. The results show that the proposed method can achieve state-of-the-art performance on diverse scene data, including driving, surveillance, and outdoor scenes. The method also demonstrates the effectiveness of the versatile pedestrian knowledge bank in enhancing pedestrian detection performance by making pedestrian features more distinguishable from background features. The method is constructed in two steps: first, constructing the versatile pedestrian knowledge bank by extracting and curating generalized pedestrian knowledge; second, leveraging the knowledge bank within a pedestrian detection framework to complement and enhance pedestrian features. The method is evaluated on various pedestrian detection datasets and shows significant improvements in detection performance. The method is also validated through ablation studies, which show that the number of knowledge features, the effects of learnable representation hints, and data variation used for constructing the knowledge bank all impact the performance of the method. The results demonstrate that the proposed method is effective in various scenarios and can be applied to different detection frameworks.This paper proposes a novel method to construct a versatile pedestrian knowledge bank for robust pedestrian detection. The method extracts generalized pedestrian knowledge from a large-scale pretrained model, such as CLIP, and curates them by quantizing the most representative features and guiding them to be distinguishable from background scenes. The knowledge bank is then used to complement and enhance pedestrian features within a pedestrian detection framework. Through comprehensive experiments, the method demonstrates its effectiveness and outperforms state-of-the-art detection performances on four public pedestrian detection datasets: CrowdHuman, WiderPedestrian, CityPersons, and Caltech. The key contributions include proposing a novel method to acquire versatile pedestrian representations applicable to diverse frameworks and scenes, constructing a versatile pedestrian knowledge bank by storing generalized and task-compatible knowledge, and verifying the effectiveness and versatility of the method with various detection frameworks and diverse datasets. The method is applicable to various detection frameworks, including region proposal based two stage detection and query based detection. The results show that the proposed method can achieve state-of-the-art performance on diverse scene data, including driving, surveillance, and outdoor scenes. The method also demonstrates the effectiveness of the versatile pedestrian knowledge bank in enhancing pedestrian detection performance by making pedestrian features more distinguishable from background features. The method is constructed in two steps: first, constructing the versatile pedestrian knowledge bank by extracting and curating generalized pedestrian knowledge; second, leveraging the knowledge bank within a pedestrian detection framework to complement and enhance pedestrian features. The method is evaluated on various pedestrian detection datasets and shows significant improvements in detection performance. The method is also validated through ablation studies, which show that the number of knowledge features, the effects of learnable representation hints, and data variation used for constructing the knowledge bank all impact the performance of the method. The results demonstrate that the proposed method is effective in various scenarios and can be applied to different detection frameworks.