The current pedestrian detection algorithm is a research hotspot in the field of driverless driving, but the pedestrian occlusion problem has not been well solved due to factors such as relatively small sample size, diverse occlusion situations, and reduced visual features. Aiming at the problem of missed detection caused by pedestrians blocking each other or pedestrians being blocked by other objects, a pedestrian detection method based on inter-frame directional gradient histogram feature correlation is proposed. First, a tracking method is added based on the YOLOv7 baseline network model to discover missed pedestrians and estimate their location information; the nearest local image containing missed pedestrians is used as the new information, using directional gradient histogram features and support vectors, a machine-based method is used to detect pedestrians at the estimated position of the missed target to improve the missed detection phenomenon caused by partial occlusion. Experimental results compared with the baseline network, the precision (P) value of this method increased by 6.25%, and the average precision (AP) of occluded pedestrians increased from 26.67% to 53.42%. Experiments show that the pedestrian detection method based on inter-frame directional gradient histogram feature correlation can improve pedestrian detection accuracy, has low computational complexity, does not significantly increase the computational overhead of the original method, and has certain application value.