Multiple Object Tracking (MOT) is a critical area within computer vision, with a broad spectrum of practical implementations. Current research has primarily focused on the development of tracking algorithms and enhancement of post-processing techniques. Yet, there has been a lack of thorough examination concerning the nature of tracking data it self. In this study, we pioneer an exploration into the distribution patterns of tracking data and identify a pronounced long-tail distribution issue within existing MOT datasets. We note a significant imbalance in the distribution of trajectory lengths across different pedestrians, a phenomenon we refer to as "pedestrians trajectory long-tail distribution". Addressing this challenge, we introduce a bespoke strategy designed to mitigate the effects of this skewed distribution. Specifically, we propose two data augmentation strategies, including Stationary Camera View Data Augmentation (SVA) and Dynamic Camera View Data Augmentation (DVA) , designed for viewpoint states and the Group Softmax (GS) module for Re-ID. SVA is to backtrack and predict the pedestrian trajectory of tail classes, and DVA is to use diffusion model to change the background of the scene. GS divides the pedestrians into unrelated groups and performs softmax operation on each group individually. Our proposed strategies can be integrated into numerous existing tracking systems, and extensive experimentation validates the efficacy of our method in reducing the influence of long-tail distribution on multi-object tracking performance. The code is available at https://github.com/chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT.

翻译：多目标跟踪（MOT）是计算机视觉中的一个关键领域，具有广泛的实际应用。当前研究主要集中于跟踪算法的开发和后处理技术的改进，然而，对跟踪数据本身性质的深入研究尚显不足。在本研究中，我们率先探索了跟踪数据的分布模式，并指出现有MOT数据集中存在的显著长尾分布问题。我们注意到不同行人的轨迹长度分布存在严重不均衡，这一现象被称为“行人轨迹长尾分布”。针对这一挑战，我们提出了一种定制策略以减轻该偏态分布的影响。具体而言，我们提出了两种数据增强策略：针对视角状态的固定视角数据增强（SVA）与动态视角数据增强（DVA），以及面向Re-ID的组Softmax（GS）模块。SVA用于回溯和预测尾类行人的轨迹，DVA则利用扩散模型改变场景背景。GS将行人划分为不相关的组，并分别对每个组进行softmax操作。我们提出的策略可集成到众多现有跟踪系统中，大量实验验证了该方法在减少长尾分布对多目标跟踪性能影响方面的有效性。代码已开源：https://github.com/chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT。