UniTracker: transformer-based CrossUnihead for multi-object tracking

In recent years, tracking-by-detection (TBD) has emerged as the predominant approach for Multi-object Tracking (MOT). Most TBD algorithms typically employ separate branch heads to handle the coarse feature representations extracted from the backbone network. However, this approach often leads to suboptimal model performance. This is due to the presence of feature conflicts among multiple tasks on one hand, and the lack of information interaction among these tasks on the other. Inspired by CSTrack and Unihead, we have combined the feature decoupling module REN with the transformer-based task interaction module CIT to form CrossUnihead which is a plug and play module. This module not only effectively achieves task-based feature decoupling, but also facilitates information interaction among different tasks. The MOT algorithm based on CrossUnihead, named UniTracker, demonstrates impressive performance on the MOT data-set when compared to other advanced methods and is capable of achieving real-time tracking.