In response to the inaccuracy of pedestrian feature recognition and the potential for misjudgment due to the lack of height information in detecting pedestrian positions using 2D LiDAR, a method for pedestrian identification based on the joint detection of 2D LiDAR and depth camera is proposed in this paper. Firstly, a Support Vector Machine (SVM) is used to filter out point cloud segments belonging to the pedestrian's leg curve in the LiDAR point cloud data, and determine the pedestrian's position. Additionally, a human body recognition algorithm is applied to the visual images to delineate pedestrians, utilizing the image coordinates of the center point of the outer body box and depth values to calculate pedestrian positions. Finally, the actual pedestrian position is derived through weighted fusion of the pedestrian position information obtained from both LiDAR and the camera. Th experimental results demonstrate that the proposed method greatly reduces the false judgement rate while inheriting the measurement accuracy of LiDAR, which proves the effectiveness of the method.