This paper addresses the long-tail distribution issue in multi-object tracking (MOT) datasets, where the number of frames for different pedestrian identities is highly imbalanced. The authors propose two data augmentation strategies, Stationary Camera View Data Augmentation (SVA) and Dynamic Camera View Data Augmentation (DVA), and a Group Softmax (GS) module to improve the performance of Re-ID in MOT. SVA is used to backtrack and predict the trajectory of tail classes, while DVA uses a diffusion model to change the scene background. The GS module groups pedestrians with similar numbers of training samples and performs softmax separately on each group to prevent the suppression of tail classes by head classes. The proposed methods are integrated into existing tracking systems and validated on four public MOT benchmarks (MOT15, MOT16, MOT17, and MOT20). The results show that the proposed methods significantly improve the performance of multi-object tracking, especially in handling the long-tail distribution issue. The code is available at https://github.com/chensi-jia/Trajectory-Long-tail-Distribution-for-MOT.This paper addresses the long-tail distribution issue in multi-object tracking (MOT) datasets, where the number of frames for different pedestrian identities is highly imbalanced. The authors propose two data augmentation strategies, Stationary Camera View Data Augmentation (SVA) and Dynamic Camera View Data Augmentation (DVA), and a Group Softmax (GS) module to improve the performance of Re-ID in MOT. SVA is used to backtrack and predict the trajectory of tail classes, while DVA uses a diffusion model to change the scene background. The GS module groups pedestrians with similar numbers of training samples and performs softmax separately on each group to prevent the suppression of tail classes by head classes. The proposed methods are integrated into existing tracking systems and validated on four public MOT benchmarks (MOT15, MOT16, MOT17, and MOT20). The results show that the proposed methods significantly improve the performance of multi-object tracking, especially in handling the long-tail distribution issue. The code is available at https://github.com/chensi-jia/Trajectory-Long-tail-Distribution-for-MOT.