# Enhancing Multiple Object Tracking Accuracy via Quantum Annealing

arXiv: https://arxiv.org/abs/2403.18908 · HTML: https://mlsanity.com/paper/2403.18908/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (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)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking](https://mlsanity.com/paper/1511.04136/) (2015)
- [TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking](https://mlsanity.com/paper/2104.00194/) (2021)
- [MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking](https://mlsanity.com/paper/2303.10404/) (2023)
- [OVTrack: Open-Vocabulary Multiple Object Tracking](https://mlsanity.com/paper/2304.08408/) (2023)
- [GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021](https://mlsanity.com/paper/2202.11983/) (2022)
- [Adiabatic Quantum Computing for Multi Object Tracking](https://mlsanity.com/paper/2202.08837/) (2022)
- [Dense Scene Multiple Object Tracking with Box-Plane Matching](https://mlsanity.com/paper/2007.15576/) (2020)
- [SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow](https://mlsanity.com/paper/2106.14259/) (2021)
- [Travel time optimization on multi-AGV routing by reverse annealing](https://mlsanity.com/paper/2204.11789/) (2022)
- [Multi Object Tracking for Predictive Collision Avoidance](https://mlsanity.com/paper/2307.02161/) (2023)
- [An Adaptive Alternating Direction Method of Multipliers](https://mlsanity.com/paper/2103.07159/) (2021)
- [EnsembleMOT: A Step towards Ensemble Learning of Multiple Object Tracking](https://mlsanity.com/paper/2210.05278/) (2022)

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

- [QANTIS: A Hardware-Validated Quantum Platform for POMDP Planning and Multi-Target Data Association](https://mlsanity.com/paper/2603.00785/) (2026)
- [QANTIS: Hardware-Calibrated Sequential POMDP Belief Updates on IBM Heron](https://mlsanity.com/paper/2607.06760/) (2026)
- [ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding](https://mlsanity.com/paper/2609.37801/) (2026)

## Abstract (arXiv)

Multiple object tracking (MOT), a key task in image recognition, presents a persistent challenge in balancing processing speed and tracking accuracy. This study introduces a novel approach that leverages quantum annealing (QA) to expedite computation speed, while enhancing tracking accuracy through the ensembling of object tracking processes. A method to improve the matching integration process is also proposed. By utilizing the sequential nature of MOT, this study further augments the tracking method via reverse annealing (RA). Experimental validation confirms the maintenance of high accuracy with an annealing time of a mere 3 μs per tracking process. The proposed method holds significant potential for real-time MOT applications, including traffic flow measurement for urban traffic light control, collision prediction for autonomous robots and vehicles, and management of products mass-produced in factories.