# Unsupervised Multiple Person Tracking using AutoEncoder-Based Lifted Multicuts

arXiv: https://arxiv.org/abs/2002.01192 · HTML: https://mlsanity.com/paper/2002.01192/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods](https://mlsanity.com/paper/2111.01604/) (2021)
- [I Spy With My Little Eye: A Minimum Cost Multicut Investigation of Dataset Frames](https://mlsanity.com/paper/2412.01296/) (2024)
- ["If you could see me through my eyes": Predicting Pedestrian Perception](https://mlsanity.com/paper/2202.13981/) (2022)
- [Unveiling the Power of Self-supervision for Multi-view Multi-human Association and Tracking](https://mlsanity.com/paper/2401.17617/) (2024)
- [ClimateVID -- Social Media Videos Analysis and Challenges Involved](https://mlsanity.com/paper/2604.27968/) (2026)

## Abstract (arXiv)

Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present an unsupervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without superivison. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an autoencoder to generate suitable latent representation. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features could be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.