To establish a robust threshold that can effectively accommodate a wide range of test datasets, it is essential to go beyond simply selecting a threshold based on a large validation set. In addition to this conventional approach, we can explore the possibility of implementing dynamic thresholds, which adapt to the specific characteristics of different datasets or even individual images. This flexibility allows for more precise and context-sensitive decision-making, potentially improving the overall performance of the model. The paper proposes a dynamic threshold learning strategy for the Deformable Part Model (DPM), aiming to enhance adaptability and efficiency in object detection. Unlike conventional methods that rely on fixed thresholds, the model adjusts its threshold in real time based on background region statistics within each image. This adaptive approach improves responsiveness to data variability and helps reduce false detections and missed objects. Experimental evaluations on benchmark datasets confirm that the proposed method maintains detection accuracy while achieving improved processing speed, making it suitable for real-world applications.