# MOTCOM: The Multi-Object Tracking Dataset Complexity Metric

arXiv: https://arxiv.org/abs/2207.10031 · HTML: https://mlsanity.com/paper/2207.10031/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (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)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (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)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [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)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [Quasi-Dense Similarity Learning for Multiple Object Tracking](https://mlsanity.com/paper/2006.06664/) (2020)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking](https://mlsanity.com/paper/2010.07548/) (2020)
- [Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking](https://mlsanity.com/paper/2007.14557/) (2020)
- [Learning by tracking: Siamese CNN for robust target association](https://mlsanity.com/paper/1604.07866/) (2016)
- [Spatial-Temporal Relation Networks for Multi-Object Tracking](https://mlsanity.com/paper/1904.11489/) (2019)
- [MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?](https://mlsanity.com/paper/2108.09518/) (2021)
- [RetinaTrack: Online Single Stage Joint Detection and Tracking](https://mlsanity.com/paper/2003.13870/) (2020)
- [Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data](https://mlsanity.com/paper/1811.07749/) (2018)
- [A survey of advances in vision-based vehicle re-identification](https://mlsanity.com/paper/1905.13258/) (2019)
- [A Unified Object Motion and Affinity Model for Online Multi-Object Tracking](https://mlsanity.com/paper/2003.11291/) (2020)
- [3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset](https://mlsanity.com/paper/2006.08466/) (2020)
- [Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking](https://mlsanity.com/paper/1704.02781/) (2017)
- [Similarity Learning Networks for Animal Individual Re-Identification -- Beyond the Capabilities of a Human Observer](https://mlsanity.com/paper/1902.09324/) (2019)
- [Spectral Metric for Dataset Complexity Assessment](https://mlsanity.com/paper/1905.07299/) (2019)
- [Measuring Dataset Granularity](https://mlsanity.com/paper/1912.10154/) (2019)

## Built on by (most cited first, 2 of 2)

- [BrackishMOT: The Brackish Multi-Object Tracking Dataset](https://mlsanity.com/paper/2302.10645/) (2023)
- [Beyond Leaderboard Scores: A Deployment-Focused Protocol for Interpretable Tracking Evaluation in Pedestrian-Centric Environments](https://mlsanity.com/paper/2610.01682/) (2026)

## Abstract (arXiv)

There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences. This lack of metrics decreases explainability, complicates comparison of datasets, and reduces the conversation on tracker performance to a matter of leader board position. As a remedy, we present the novel MOT dataset complexity metric (MOTCOM), which is a combination of three sub-metrics inspired by key problems in MOT: occlusion, erratic motion, and visual similarity. The insights of MOTCOM can open nuanced discussions on tracker performance and may lead to a wider acknowledgement of novel contributions developed for either less known datasets or those aimed at solving sub-problems. We evaluate MOTCOM on the comprehensive MOT17, MOT20, and MOTSynth datasets and show that MOTCOM is far better at describing the complexity of MOT sequences compared to the conventional density and number of tracks. Project page at https://vap.aau.dk/motcom