Abstract: The perception system of intelligent autonomous vehicles,serving as the frontend input,plays a crucial role in the functional application of these vehicles.However,existing sensors have blind spots in their field of view,leading to a lack of sensitivity towards close-range targets,which can potentially cause safety issues.Thus,effective recognition and tracking of near-distance targets have become pressing issues to address.This paper proposes an adaptive dual recognition technique that employs the current mainstream target detectors and trackers and combines them with re-identification technology for real-time tracking of nearby pedestrians.This method compensates for the shortcomings of current deep learning algorithms that are heavily dependent on datasets,improves the versatility of the algorithms,expands the perception range of autonomous vehicles,and enhances their commercial value.Actual vehicle experiments demonstrate that the algorithm can continuously track pedestrians from far to near and vice versa,while maintaining a stable pedestrian ID.