Hierarchical Matching Multi-Object Tracking Algorithm Based on Pseudo-Depth Information
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.
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