# Simple Unsupervised Multi-Object Tracking

arXiv: https://arxiv.org/abs/2006.02609 · HTML: https://mlsanity.com/paper/2006.02609/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 30 of 33)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Simple Cues Lead to a Strong Multi-Object Tracker](https://mlsanity.com/paper/2206.04656/) (2022)
- [Learning a Proposal Classifier for Multiple Object Tracking](https://mlsanity.com/paper/2103.07889/) (2021)
- [TransCenter: Transformers with Dense Representations for Multiple-Object Tracking](https://mlsanity.com/paper/2103.15145/) (2021)
- [Learnable Online Graph Representations for 3D Multi-Object Tracking](https://mlsanity.com/paper/2104.11747/) (2021)
- [One More Check: Making "Fake Background" Be Tracked Again](https://mlsanity.com/paper/2104.09441/) (2021)
- [Self-Supervised Multi-Object Tracking with Cross-Input Consistency](https://mlsanity.com/paper/2111.05943/) (2021)
- [TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes](https://mlsanity.com/paper/2308.11157/) (2023)
- [Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking](https://mlsanity.com/paper/2105.02426/) (2021)
- [Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation](https://mlsanity.com/paper/2201.01297/) (2022)
- [Self-Supervised Multi-Object Tracking with Path Consistency](https://mlsanity.com/paper/2404.05136/) (2024)
- [Uncertainty-aware Unsupervised Multi-Object Tracking](https://mlsanity.com/paper/2307.15409/) (2023)
- [Local Metrics for Multi-Object Tracking](https://mlsanity.com/paper/2104.02631/) (2021)
- [Online and Real-Time Tracking in a Surveillance Scenario](https://mlsanity.com/paper/2106.01153/) (2021)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [Tracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning](https://mlsanity.com/paper/2309.00942/) (2023)
- [SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow](https://mlsanity.com/paper/2106.14259/) (2021)
- [Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales](https://mlsanity.com/paper/2304.13147/) (2023)
- [Leveraging Foundation Models via Knowledge Distillation in Multi-Object Tracking: Distilling DINOv2 Features to FairMOT](https://mlsanity.com/paper/2407.18288/) (2024)
- [COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking](https://mlsanity.com/paper/2310.03006/) (2023)
- [Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs](https://mlsanity.com/paper/2409.17221/) (2024)
- [Learning data association without data association: An EM approach to neural assignment prediction](https://mlsanity.com/paper/2105.00369/) (2021)
- [Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations](https://mlsanity.com/paper/2210.13570/) (2022)
- [UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation](https://mlsanity.com/paper/2306.09613/) (2023)
- [Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation](https://mlsanity.com/paper/2312.04167/) (2023)
- [Unveiling the Power of Self-supervision for Multi-view Multi-human Association and Tracking](https://mlsanity.com/paper/2401.17617/) (2024)
- [Learning a Neural Association Network for Self-supervised Multi-Object Tracking](https://mlsanity.com/paper/2411.11514/) (2024)
- [Localization-Based Tracking](https://mlsanity.com/paper/2104.05823/) (2021)
- [Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder](https://mlsanity.com/paper/2202.09315/) (2022)
- [Real-time Multi-Object Tracking Based on Bi-directional Matching](https://mlsanity.com/paper/2303.08444/) (2023)

## Abstract (arXiv)

Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets. In this work, we remove the need for annotated datasets by proposing an unsupervised re-identification network, thus sidestepping the labeling costs entirely, required for training. Given unlabeled videos, our proposed method (SimpleReID) first generates tracking labels using SORT and trains a ReID network to predict the generated labels using crossentropy loss. We demonstrate that SimpleReID performs substantially better than simpler alternatives, and we recover the full performance of its supervised counterpart consistently across diverse tracking frameworks. The observations are unusual because unsupervised ReID is not expected to excel in crowded scenarios with occlusions, and drastic viewpoint changes. By incorporating our unsupervised SimpleReID with CenterTrack trained on augmented still images, we establish a new state-of-the-art performance on popular datasets like MOT16/17 without using tracking supervision, beating current best (CenterTrack) by 0.2-0.3 MOTA and 4.4-4.8 IDF1 scores. We further provide evidence for limited scope for improvement in IDF1 scores beyond our unsupervised ReID in the studied settings. Our investigation suggests reconsideration towards more sophisticated, supervised, end-to-end trackers by showing promise in simpler unsupervised alternatives.