# Simple Cues Lead to a Strong Multi-Object Tracker

arXiv: https://arxiv.org/abs/2206.04656 · HTML: https://mlsanity.com/paper/2206.04656/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 72 of its 99 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 30 of 72)

- [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)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [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)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [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)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [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)
- [Improving robustness against common corruptions by covariate shift adaptation](https://mlsanity.com/paper/2006.16971/) (2020)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Revisiting Batch Normalization For Practical Domain Adaptation](https://mlsanity.com/paper/1603.04779/) (2016)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)

## Built on by (most cited first, 30 of 44)

- [Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking](https://mlsanity.com/paper/2308.00783/) (2023)
- [Multiple Object Tracking as ID Prediction](https://mlsanity.com/paper/2403.16848/) (2024)
- [UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation](https://mlsanity.com/paper/2312.08952/) (2023)
- [EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View](https://mlsanity.com/paper/2310.13350/) (2023)
- [Lifting Multi-View Detection and Tracking to the Bird's Eye View](https://mlsanity.com/paper/2403.12573/) (2024)
- [Towards Generalizable Multi-Object Tracking](https://mlsanity.com/paper/2406.00429/) (2024)
- [Keypoint Promptable Re-Identification](https://mlsanity.com/paper/2407.18112/) (2024)
- [DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions](https://mlsanity.com/paper/2309.04682/) (2023)
- [DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking](https://mlsanity.com/paper/2403.02767/) (2024)
- [MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking](https://mlsanity.com/paper/2403.10826/) (2024)
- [SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow](https://mlsanity.com/paper/2404.11426/) (2024)
- [Zero-Shot 4D Lidar Panoptic Segmentation](https://mlsanity.com/paper/2504.00848/) (2025)
- [Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking](https://mlsanity.com/paper/2311.10382/) (2023)
- [Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown](https://mlsanity.com/paper/2409.09293/) (2024)
- [Is Multiple Object Tracking a Matter of Specialization?](https://mlsanity.com/paper/2411.00553/) (2024)
- [CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object Tracking](https://mlsanity.com/paper/2505.01257/) (2025)
- [DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy](https://mlsanity.com/paper/2407.17272/) (2024)
- [NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking](https://mlsanity.com/paper/2509.02111/) (2025)
- [Multi-Granularity Language-Guided Training for Multi-Object Tracking](https://mlsanity.com/paper/2406.04844/) (2024)
- [DroneMOT: Drone-based Multi-Object Tracking Considering Detection Difficulties and Simultaneous Moving of Drones and Objects](https://mlsanity.com/paper/2407.09051/) (2024)
- [When Trackers Date Fish: A Benchmark and Framework for Underwater Multiple Fish Tracking](https://mlsanity.com/paper/2507.06400/) (2025)
- [SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop](https://mlsanity.com/paper/2508.12813/) (2025)
- [AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking](https://mlsanity.com/paper/2401.13950/) (2024)
- [Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking](https://mlsanity.com/paper/2410.02638/) (2024)
- [FusionSORT: Fusion Methods for Online Multi-object Visual Tracking](https://mlsanity.com/paper/2501.00843/) (2025)
- [Tiny Neural Networks for Multi-Object Tracking in a Modular Kalman Framework](https://mlsanity.com/paper/2504.02519/) (2025)
- [Depth-Aware Scoring and Hierarchical Alignment for Multiple Object Tracking](https://mlsanity.com/paper/2506.00774/) (2025)
- [GRASPTrack: Geometry-Reasoned Association via Segmentation and Projection for Multi-Object Tracking](https://mlsanity.com/paper/2508.08117/) (2025)
- [Occlusion-Aware SORT: Observing Occlusion for Robust Multi-Object Tracking](https://mlsanity.com/paper/2603.06034/) (2026)
- [Hierarchical IoU Tracking based on Interval](https://mlsanity.com/paper/2406.13271/) (2024)

## Abstract (arXiv)

For a long time, the most common paradigm in Multi-Object Tracking was tracking-by-detection (TbD), where objects are first detected and then associated over video frames. For association, most models resourced to motion and appearance cues, e.g., re-identification networks. Recent approaches based on attention propose to learn the cues in a data-driven manner, showing impressive results. In this paper, we ask ourselves whether simple good old TbD methods are also capable of achieving the performance of end-to-end models. To this end, we propose two key ingredients that allow a standard re-identification network to excel at appearance-based tracking. We extensively analyse its failure cases, and show that a combination of our appearance features with a simple motion model leads to strong tracking results. Our tracker generalizes to four public datasets, namely MOT17, MOT20, BDD100k, and DanceTrack, achieving state-of-the-art performance. https://github.com/dvl-tum/GHOST.