A hierarchical matching multi-object tracking algorithm based on pseudo-depth information was proposed to address the performance limitations of traditional multi-object tracking methods that rely on intersection over union (IOU) for association under target occlusion, as well as the constraints of feature re-identification in dealing with visually similar objects. The proposed algorithm utilized a stereo geometric approach to acquire pseudo-depth information of objects in the image. Based on the magnitude of pseudo-depth, both the detection boxes and trajectories were divided into multiple distinct subsets. When some objects were occluded but had significant differences in pseudo-depth, they were classified into different pseudo-depth levels, thereby avoiding matching conflicts. Subsequently, a pseudo-depth cost matrix was computed using the pseudo-depth information, and an IOU pseudo-depth (IOU-D) matching was performed within the same pseudo-depth level to associate occluded targets located at the same pseudo-depth level. Experimental results show that the proposed algorithm achieved 65.1% and 58.5% higher order tracking accuracy (HOTA) on the MOT17 and DanceTrack test sets, respectively. Compared to the baseline model, ByteTrack, the proposed algorithm improved by 2.0% and 10.8% on the two data sets, respectively. Experimental results indicate that effectively utilizing the potential pseudo-depth information in the image can significantly enhance the tracking accuracy of occluded targets.