Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 168 tok/s
Gemini 2.5 Pro 44 tok/s Pro
GPT-5 Medium 33 tok/s Pro
GPT-5 High 28 tok/s Pro
GPT-4o 106 tok/s Pro
Kimi K2 181 tok/s Pro
GPT OSS 120B 446 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Enhancing Multiple Object Tracking Accuracy via Quantum Annealing (2403.18908v1)

Published 27 Mar 2024 in cs.CV, eess.IV, and quant-ph

Abstract: 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 $\mu$s 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (35)
  1. Lasot: A high-quality benchmark for large-scale single object tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5374–5383, 2019.
  2. Learning feature embeddings for discriminant model based tracking. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16, pages 759–775. Springer, 2020.
  3. Global data association for multi-object tracking using network flows. In 2008 IEEE conference on computer vision and pattern recognition, pages 1–8. IEEE, 2008.
  4. Learning to associate: Hybridboosted multi-target tracker for crowded scene. In 2009 IEEE conference on computer vision and pattern recognition, pages 2953–2960. IEEE, 2009.
  5. Simple online and realtime tracking. In 2016 IEEE international conference on image processing (ICIP), pages 3464–3468. IEEE, 2016.
  6. Simple online and realtime tracking with a deep association metric. In 2017 IEEE international conference on image processing (ICIP), pages 3645–3649. IEEE, 2017.
  7. Bytetrack: Multi-object tracking by associating every detection box. In European Conference on Computer Vision, pages 1–21. Springer, 2022.
  8. Video-based vehicle counting framework. IEEE Access, 7:64460–64470, 2019.
  9. Bruk Gebregziabher. Multi object tracking for predictive collision avoidance. arXiv preprint arXiv:2307.02161, 2023.
  10. Multiple object tracking for video-based sports analysis. In CEUR workshop proceedings: IVUS 2021: Information society and university studies 2021: Proceedings of the 26th international conference on information society and university studies (IVUS 2021), Kaunas, Lithuania, April 23, 2021, volume 2915, pages 1–10. CEUR-WS, 2021.
  11. Real-time vision-based multiple object tracking of a production process: Industrial digital twin case study. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235(11):1861–1872, 2021.
  12. Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83–97, 1955.
  13. James Munkres. Algorithms for the assignment and transportation problems. Journal of the society for industrial and applied mathematics, 5(1):32–38, 1957.
  14. A quantum adiabatic evolution algorithm applied to random instances of an np-complete problem. Science, 292(5516):472–475, 2001.
  15. Robustness of adiabatic quantum computation. Physical Review A, 65(1):012322, 2001.
  16. Adiabatic quantum computing for multi object tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8811–8822, 2022.
  17. Multiple target tracking and filtering using bayesian diabatic quantum annealing. In 2022 Sensor Data Fusion: Trends, Solutions, Applications (SDF), pages 1–9. IEEE, 2022a.
  18. Implementation of a multiple target tracking filter on an adiabatic quantum computer. In 2022 IEEE Aerospace Conference (AERO), pages 1–14. IEEE, 2022b.
  19. Andrew Lucas. Ising formulations of many np problems. Frontiers in physics, 2:5, 2014.
  20. Quantum annealing in the transverse ising model. Physical Review E, 58(5):5355, 1998.
  21. Dense scene multiple object tracking with box-plane matching. In Proceedings of the 28th ACM International Conference on Multimedia, pages 4615–4619, 2020.
  22. Giaotracker: A comprehensive framework for mcmot with global information and optimizing strategies in visdrone 2021. In Proceedings of the IEEE/CVF International conference on computer vision, pages 2809–2819, 2021.
  23. Ensemblemot: A step towards ensemble learning of multiple object tracking. arXiv preprint arXiv:2210.05278, 2022.
  24. Structure-based protein nmr assignments using native structural ensembles. Journal of Biomolecular NMR, 40:263–276, 2008.
  25. Experimental evaluation of performance improvement by sparse operation in ising spin computing. In 2022 IEEE Nanotechnology Materials and Devices Conference (NMDC), pages 29–32. IEEE, 2022.
  26. Ising computation based combinatorial optimization using spin-hall effect (she) induced stochastic magnetization reversal. Journal of applied physics, 121(19), 2017.
  27. Adiabatic quantum computation. Reviews of Modern Physics, 90(1):015002, 2018.
  28. The UA-DETRAC Benchmark Suite. [Online]. Available: https://detrac-db.rit.albany.edu/. [Accessed: 14 - Jul - 2023].
  29. Ua-detrac: A new benchmark and protocol for multi-object detection and tracking. Computer Vision and Image Understanding, 193:102907, 2020.
  30. Rihamark: perceptual image hash benchmarking. In Media watermarking, security, and forensics III, volume 7880, pages 343–357. SPIE, 2011.
  31. Evaluating multiple object tracking performance: the clear mot metrics. EURASIP Journal on Image and Video Processing, 2008:1–10, 2008.
  32. Performance measures and a data set for multi-target, multi-camera tracking. In European conference on computer vision, pages 17–35. Springer, 2016.
  33. Reverse quantum annealing of the p-spin model with relaxation. Physical Review A, 101(2):022331, 2020.
  34. Travel time optimization on multi-agv routing by reverse annealing. Scientific reports, 12(1):17753, 2022.
  35. Reverse Quantum Annealing for Local Refinement of Solutions (white paper). [Online]. Available: https://www.dwavesys.com/media/5hsjmvom/14-1018a-a_reverse_quantum_annealing_for_local_ refinement_of_solutions.pdf. [Accessed: 14 - Jul - 2023].

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Authors (1)

  1. Yasuyuki Ihara
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 2 tweets and received 2 likes.

Upgrade to Pro to view all of the tweets about this paper:

Start a free 7-day Pro trial