# Towards Generalizable Multi-Object Tracking

**Authors:** Zheng Qin, Le Wang, Sanping Zhou, Panpan Fu, Gang Hua, Wei Tang  
**arXiv:** [2406.00429](https://arxiv.org/abs/2406.00429)  
**Date:** 2024-06-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2406.00429v1/

## Key points

- Authors emphasize the importance of generalizability in Multi-Object Tracking (MOT)
- Customization in association information for motion and appearance is crucial for different scenarios
- In-depth investigation into factors influencing tracker generalization across scenarios
- Factors distilled into tracking scenario attributes for designing versatile trackers
- Introduction of GeneralTrack framework designed to generalize effectively across diverse scenarios
- GeneralTrack framework showcases superior generalizability and achieves state-of-the-art performance on multiple benchmarks
- Potential for domain generalization in tracking applications

## Summary

In their paper titled "Towards Generalizable Multi-Object Tracking," authors Zheng Qin, Le Wang, Sanping Zhou, Panpan Fu, Gang Hua, and Wei Tang delve into the challenges faced by existing trackers in accommodating various tracking scenarios within Multi-Object Tracking (MOT). They emphasize the importance of trackers demonstrating a high level of generalizability across diverse scenarios to avoid narrowly tailored solutions with limited applicability. The authors identify the need for customization in association information related to motion and appearance for different scenarios. This often requires hypothesis testing and experimentation. To address these challenges, the authors conduct an in-depth investigation into the factors influencing tracker generalization across different scenarios. They distill these factors into a set of tracking scenario attributes that can serve as guidelines for designing more versatile and generalizable trackers. Additionally, they introduce a novel framework called GeneralTrack . This framework is designed to generalize effectively across diverse scenarios without the need to balance motion and appearance explicitly. The proposed GeneralTrack framework showcases superior generalizability compared to existing methods and achieves state-of-the-art performance on multiple benchmarks. The authors also highlight its potential for domain generalization in tracking applications. By offering a comprehensive analysis of tracker generalization factors and introducing an innovative tracking framework .