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
this https URL

本研究探索了多目标跟踪数据的分布模式，并发现现有多目标跟踪数据集中存在显著的长尾分布问题。针对这一挑战，我们提出了两种数据增强策略，并引入了 Group Softmax 模块用于 Re-ID，在多个现有跟踪系统中集成我们的方法，经过广泛实验验证了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。