Delving into the Trajectory Long-tail Distribution for Muti-object
  Tracking
Sijia Chen En Yu Jinyang Li Wenbing Tao
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.