# GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021

arXiv: https://arxiv.org/abs/2202.11983 · HTML: https://mlsanity.com/paper/2202.11983/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 80 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 30 of 45)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://mlsanity.com/paper/2006.02334/) (2020)
- [Quasi-Dense Similarity Learning for Multiple Object Tracking](https://mlsanity.com/paper/2006.06664/) (2020)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [Learning a Neural Solver for Multiple Object Tracking](https://mlsanity.com/paper/1912.07515/) (2019)
- [Detect to Track and Track to Detect](https://mlsanity.com/paper/1710.03958/) (2017)
- [Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking](https://mlsanity.com/paper/2007.14557/) (2020)
- [Vision Meets Drones: A Challenge](https://mlsanity.com/paper/1804.07437/) (2018)

## Built on by (most cited first, 30 of 46)

- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory](https://mlsanity.com/paper/2411.11922/) (2024)
- [MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking](https://mlsanity.com/paper/2303.10404/) (2023)
- [UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation](https://mlsanity.com/paper/2312.08952/) (2023)
- [CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools](https://mlsanity.com/paper/2312.07352/) (2023)
- [The 8th AI City Challenge](https://mlsanity.com/paper/2404.09432/) (2024)
- [1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results](https://mlsanity.com/paper/2211.13508/) (2022)
- [Video-based Sequential Bayesian Homography Estimation for Soccer Field Registration](https://mlsanity.com/paper/2311.10361/) (2023)
- [FeatureSORT: Essential Features for Effective Tracking](https://mlsanity.com/paper/2407.04249/) (2024)
- [AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture](https://mlsanity.com/paper/2309.13393/) (2023)
- [The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024](https://mlsanity.com/paper/2311.14762/) (2023)
- [TrackSSM: A General Motion Predictor by State-Space Model](https://mlsanity.com/paper/2409.00487/) (2024)
- [No Train Yet Gain: Towards Generic Multi-Object Tracking in Sports and Beyond](https://mlsanity.com/paper/2506.01373/) (2025)
- [MVTrajecter: Multi-View Pedestrian Tracking with Trajectory Motion Cost and Trajectory Appearance Cost](https://mlsanity.com/paper/2509.01157/) (2025)
- [UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking](https://mlsanity.com/paper/2402.12303/) (2024)
- [Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art](https://mlsanity.com/paper/2506.13457/) (2025)
- [FastTracker: Real-Time and Accurate Visual Tracking](https://mlsanity.com/paper/2508.14370/) (2025)
- [Enhancing Multiple Object Tracking Accuracy via Quantum Annealing](https://mlsanity.com/paper/2403.18908/) (2024)
- [SurgiTrack: Fine-Grained Multi-Class Multi-Tool Tracking in Surgical Videos](https://mlsanity.com/paper/2405.20333/) (2024)
- [PAMI-AD: An Activity Detector Exploiting Part-attention and Motion Information in Surveillance Videos](https://mlsanity.com/paper/2203.03796/) (2022)
- [CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching](https://mlsanity.com/paper/2307.02753/) (2023)
- [SFSORT: Scene Features-based Simple Online Real-Time Tracker](https://mlsanity.com/paper/2404.07553/) (2024)
- [Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis](https://mlsanity.com/paper/2407.15199/) (2024)
- [Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines](https://mlsanity.com/paper/2410.14093/) (2024)
- [SFTrack: A Robust Scale and Motion Adaptive Algorithm for Tracking Small and Fast Moving Objects](https://mlsanity.com/paper/2410.20079/) (2024)
- [SportMamba: Adaptive Non-Linear Multi-Object Tracking with State Space Models for Team Sports](https://mlsanity.com/paper/2506.03335/) (2025)
- [Physical ID-Transfer Attacks against Multi-Object Tracking via Adversarial Trajectory](https://mlsanity.com/paper/2512.01934/) (2025)
- [EnsembleMOT: A Step towards Ensemble Learning of Multiple Object Tracking](https://mlsanity.com/paper/2210.05278/) (2022)
- [One Homography is All You Need: IMM-based Joint Homography and Multiple Object State Estimation](https://mlsanity.com/paper/2409.02562/) (2024)

## Abstract (arXiv)

In recent years, algorithms for multiple object tracking tasks have benefited from great progresses in deep models and video quality. However, in challenging scenarios like drone videos, they still suffer from problems, such as small objects, camera movements and view changes. In this paper, we propose a new multiple object tracker, which employs Global Information And some Optimizing strategies, named GIAOTracker. It consists of three stages, i.e., online tracking, global link and post-processing. Given detections in every frame, the first stage generates reliable tracklets using information of camera motion, object motion and object appearance. Then they are associated into trajectories by exploiting global clues and refined through four post-processing methods. With the effectiveness of the three stages, GIAOTracker achieves state-of-the-art performance on the VisDrone MOT dataset and wins the 3rd place in the VisDrone2021 MOT Challenge.