# Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object Tracking

arXiv: https://arxiv.org/abs/2301.10559 · HTML: https://mlsanity.com/paper/2301.10559/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 59 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 27 of 27)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [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)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (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)
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://mlsanity.com/paper/1803.03243/) (2018)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Strong-Weak Distribution Alignment for Adaptive Object Detection](https://mlsanity.com/paper/1812.04798/) (2018)
- [Domain Generalization for Object Recognition with Multi-task Autoencoders](https://mlsanity.com/paper/1508.07680/) (2015)
- [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1705.00609/) (2017)
- [Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies](https://mlsanity.com/paper/1701.01909/) (2017)
- [Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor](https://mlsanity.com/paper/1504.02340/) (2015)
- [Deep Network Flow for Multi-Object Tracking](https://mlsanity.com/paper/1706.08482/) (2017)
- [Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking](https://mlsanity.com/paper/2012.02337/) (2020)
- [Online Domain Adaptation for Multi-Object Tracking](https://mlsanity.com/paper/1508.00776/) (2015)
- [Tracking all members of a honey bee colony over their lifetime](https://mlsanity.com/paper/1802.03192/) (2018)
- [A dataset of ant colonies motion trajectories in indoor and outdoor scenes for social cluster behavior study](https://mlsanity.com/paper/2204.04380/) (2022)

## Built on by (most cited first, 3 of 3)

- [CHOTA: A Higher Order Accuracy Metric for Cell Tracking](https://mlsanity.com/paper/2408.11571/) (2024)
- [QTrack: Query-Driven Reasoning for Multi-modal MOT](https://mlsanity.com/paper/2603.13759/) (2026)
- [ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding](https://mlsanity.com/paper/2609.37801/) (2026)

## Abstract (arXiv)

Tracking individuals is a vital part of many experiments conducted to understand collective behaviour. Ants are the paradigmatic model system for such experiments but their lack of individually distinguishing visual features and their high colony densities make it extremely difficult to perform reliable tracking automatically. Additionally, the wide diversity of their species' appearances makes a generalized approach even harder. In this paper, we propose a data-driven multi-object tracker that, for the first time, employs domain adaptation to achieve the required generalisation. This approach is built upon a joint-detection-and-tracking framework that is extended by a set of domain discriminator modules integrating an adversarial training strategy in addition to the tracking loss. In addition to this novel domain-adaptive tracking framework, we present a new dataset and a benchmark for the ant tracking problem. The dataset contains 57 video sequences with full trajectory annotation, including 30k frames captured from two different ant species moving on different background patterns. It comprises 33 and 24 sequences for source and target domains, respectively. We compare our proposed framework against other domain-adaptive and non-domain-adaptive multi-object tracking baselines using this dataset and show that incorporating domain adaptation at multiple levels of the tracking pipeline yields significant improvements. The code and the dataset are available at https://github.com/chamathabeysinghe/da-tracker.