Abstract: In complex scenarios,multi-target tracking faces problems such as dense target occlusion,nonlinear target motion,poor association matching algorithms leading to identity matching errors,and frequent identity switching.This paper takes ByteTrack as the baseline algorithm and improves its association strategy from three aspects:motion model,weak feature data association,and matching algorithm by fully utilizing existing discriminative features.A multi-objective tracking algorithm with multi discri-minative feature enhanced association strategy is proposed.Firstly,in response to the problem that conventional Kalman filtering is difficult to predict the target position of nonlinear motion,the noise covariance of Kalman filtering is dynamically adjusted using prediction similarity and detection confidence to optimize the motion model and improve the accuracy of target position prediction.Secondly,by integrating a secondary association algorithm,weak feature data association is performed between low-confidence detections and tracks that remain unmatched after the initial association,reducing mismatches between them.Finally,for low confidence detection targets,relative depth is used to decompose the detection targets and trajectories,and a cascaded matching algorithm is used for association,effectively reducing IoU matching collisions and improving the tracking performance of the algorithm in dense occlusion scenes.On the MOT17 and MOT20 test sets,HOTA is 64.5% and 63.2%,respectively,and all evaluation metrics show significant improvement compared to the baseline algorithm.

Key words: Multi-object tracking, Joint detection and tracking, Data association