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A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11

Year 2025, Volume: 8 Issue: 2, 363 - 370, 15.03.2025

Cevahir Parlak

https://doi.org/10.34248/bsengineering.1596008

Abstract

This study sheds light on the daunting task of single-object tracking using state-of-the-art BoT-SORT, DeepSORT, SORT, and ByteTrack tracking algorithms with YOLO-NAS and YOLO11 object detectors. Object tracking is a step further of object detection and tries to detect the movement of objects in video files and it has enormous range of real-world application fields. Object tracking also assigns unique identifiers to each tracked object and tries to maintain the identity throughout the entire sequence. Current models can achieve great success in object tracking, however there are still too many obstacles and challenges lying ahead to resolve. YOLO-NAS and YOLO11 are the latest and most used object detection models. YOLO can be combined with different tracking methods such as ByteTrack, BoT-SORT, SORT, and DeepSORT for object tracking. The advantage of YOLO is its extremely fast implementation compared to the other methods. When accompanied by specialized tracking algorithms, YOLO achieves the best scores in object tracking. This study focuses on the implementation of YOLO-NAS and YOLO11 in tracking and results demonstrate that YOLO11 is more accurate and stable with BoT-SORT, however, it is faster using ByteTrack method.

Keywords

Single-Object tracking YOLO BoT-SORT ByteTrack SORT DeepSORT

Ethical Statement

Ethics committee approval was not required for this study because there was no study on animals or humans.

References

  • Aharon N, Orfaig R, Bobrovsky BZ. 2022. BoT-SORT: Robust associations multi-pedestrian tracking. arXiv, 2206: 14651.
  • Aharon S, Dupont L, Masad O, Yurkova K, Fridman L, Lkdci, Khvedchenya E, Rubin R, Bagrov N, Tymchenko B, Keren T, Zhilko A, Deci E. 2021. Supergradients. Github Repository, URL: https://github.com/Deci-AI/super-gradients (accessed date: December 4, 2024).
  • Atalı G, Eyüboğlu M. 2022. A study on object detection and tracking of a mobile robot using CIE L*a*b* color space. Düzce Uni J Sci Technol, 10(5): 77-90.
  • Babenko B, Yang MH, Belongie S. 2009. Visual tracking with online multiple instance learning. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, Miami FL, USA, p: 983-990.
  • Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS. 2016. Fully-Convolutional Siamese networks for object tracking. In: Proceedings of Computer Vision–ECCV 2016 Workshops: Proceedings, Springer International Publishing Part II 14, October 8-10 and 15-16, Amsterdam, the Netherlands, p: 850-865.
  • Bewley A, Ge Z, Ott L, Ramos F, Upcroft B. 2016. Simple online and realtime tracking. In: Proceedings of 2016 IEEE International Conference on Image Processing (ICIP), September 20-25, Phoenix AZ, USA, p: 3464-3468.
  • Black MJ, Anandan P. 1993. A framework for the robust estimation of optical flow. In: Proceedings of 1993 4th International Conference on Computer Vision, May 11-14, Berlin, Germany, p: 231-236.
  • Bolme DS, Beveridge JR, Draper BA, Lui YM. 2010. Visual object tracking using adaptive correlation filters. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, San Francisco CA, USA, p: 2544-2550.
  • Bradski GR. 1998. Computer vision face tracking for use in a perceptual user interface. Intel Technol J, 2.
  • Dalal N, Triggs B. 2005. Histograms of oriented gradients for human detection. In: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) 1, June 20-25, San Diego CA, USA, p: 886-893.
  • Du Y, Zhao Z, Song Y, Zhao Y, Su F, Gong T, Meng H. 2023. Strongsort: Make deepsort great again. IEEE Trans Multimedia, 25: 8725-8737.
  • Fan H, Bai H, Lin L, Yang F, Chu P, Deng G, ... Ling H. 2021. LaSOT: A high-quality large-scale single object tracking benchmark. Int J Comput Vis, 129: 439-461.
  • Freund Y, Schapire RE. 1996. Experiments with a new boosting algorithm. In: Proceedings of 13th International Conference on Machine Learning, July 3-6, Bari, Italy, p: 148-156.
  • Fukunaga K, Hostetler LD. 1975. The Estimation of the gradient of a density function, with applications in pattern-recognition. IERE Trans Inf Theory, 21(1): 32-40.
  • Grabner H, Grabner M, Bischof H. 2006. Real-time tracking via on-line boosting. In: Proceedings of the British Machine Vision Conference 2006, British Machine Vision Association BMVA, September 4-7, Edinburg, UK, p: 47–56.
  • Havuç E, Alpak Ş, Çakırel G, Baran MK. 2021. Ping-pong ball tracking through deep learning, Eur J Sci Technol, 27: 629-635.
  • Held D, Thrun S, Savarese S. 2016. Learning to track at 100 fps with deep regression networks. In: Proceedings of Computer Vision–ECCV 2016: 14th European Conference Part I 14 Springer International Publishing, October 11–14, Amsterdam, the Netherlands, p: 749-765.
  • Henriques JF, Caseiro R, Martins P, Batista J. 2014. High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell, 37(3): 583-596.
  • Hu S, Zhao X, Huang K. 2024. SOTVerse: A user-defined task space of single object tracking. Int J Comput Vis, 132(3): 872-930.
  • Jocher G, Qiu J. 2024. Ultralytics YOLO11. URL: https://github.com/ultralytics/ultralytics, (accessed date: December 4, 2024).5
  • Kadam P, Fang G, Zou JJ. 2024. Object tracking using computer vision: A review. Comput, 13(6): 136.
  • Kalal Z, Mikolajczyk K, Matas J. 2010. Forward-backward error: Automatic detection of tracking failures. In: Proceedings of 2010 20th International Conference on Pattern Recognition, August 23–26, Istanbul, Türkiye, p: 2756-2759.
  • Kalal Z, Mikolajczyk K, Matas J. 2012. Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell, 34(7): 1409-1422.
  • Kang, B., Chen, X., Lai, S., Liu, Y., Liu, Y., & Wang, D. 2024. Exploring enhanced contextual information for video-level object tracking. arXiv preprint arXiv:2412.11023.
  • Kim J, Cho J. 2021. A set of single YOLO modalities to detect occluded entities via viewpoint conversion. Appl Sci, 11(13): 6016.
  • Lin L, Fan H, Zhang Z, Wang Y, Xu Y, Ling H. 2025. Tracking meets LoRA: Faster training, larger model, stronger performance. In: Proceedings of European Conference on Computer Vision, Jan 15-16, London, UK, Springer, Cham, p: 300-318.
  • Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL. 2014. Microsoft COCO: Common objects in context. In: Proceedings of Computer Vision–ECCV 2014: 13th European Conference, Proceedings, Part V 13, Springer International Publishing, September 6-12, Zurich, Switzerland, p: 740-755.
  • Lukezic A, Vojir T, Cehovin ZL, Matas J, Kristan M. 2017. Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, Honolulu HI, USA, p: 6309-6318.
  • Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S. 2019. PyTorch: An imperative style, high-performance deep learning library. In: Proceedings of Advances in Neural Information Processing Systems 32, December 8-14, Vancouver, Canada, p: 8026–8037.
  • Redmon J. 2016. You Only Look Once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, Las Vegas NV, USA, p: 779-788.
  • Rossum GV. 2007. Python programming language. In: Proceedings of USENIX Annual Technical Conference 41:1, June 17-22, Santa Clara CA, USA, p: 1-36.
  • Soleimanitaleb Z, Keyvanrad MA. 2022. Single object tracking: A survey of methods, datasets, and evaluation metrics. arXiv preprint arXiv:2201.13066.
  • Şimşek M, Tekbaş MK. 2024. Heatmap creation with YOLO-Deep SORT system customized for in-store customer behavior analysis. Commun Fac Sci Univ Ank Series A2-A3 Phys Sci and Eng, 66(1): 118-131.
  • Tan FG, Yüksel AS, Aydemir E, Ersoy M. 2021. A review on object detection and tracking with deep learning techniques. Eur J Sci Technol, 25: 159-171.
  • Wojke N, Bewley A, Paulus D. 2017. Simple online and realtime tracking with a deep association metric. In: Proceedings of 2017 IEEE International Conference on Image Processing (ICIP), September 17-20, Beijing, China, pp: 3645-3649.
  • Zhang Y, Sun P, Jiang Y, Yu D, Weng F, Yuan Z, Luo P, Liu W, Wang X. 2022. Bytetrack: Multi-object tracking by associating every detection box. In: Proceedings of European Conference on Computer Vision, Cham: Springer Nature Switzerland, October 23–27, Tel Aviv, Israel, p: 1-21.

A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11

Year 2025, Volume: 8 Issue: 2, 363 - 370, 15.03.2025

Cevahir Parlak

https://doi.org/10.34248/bsengineering.1596008

Abstract

This study sheds light on the daunting task of single-object tracking using state-of-the-art BoT-SORT, DeepSORT, SORT, and ByteTrack tracking algorithms with YOLO-NAS and YOLO11 object detectors. Object tracking is a step further of object detection and tries to detect the movement of objects in video files and it has enormous range of real-world application fields. Object tracking also assigns unique identifiers to each tracked object and tries to maintain the identity throughout the entire sequence. Current models can achieve great success in object tracking, however there are still too many obstacles and challenges lying ahead to resolve. YOLO-NAS and YOLO11 are the latest and most used object detection models. YOLO can be combined with different tracking methods such as ByteTrack, BoT-SORT, SORT, and DeepSORT for object tracking. The advantage of YOLO is its extremely fast implementation compared to the other methods. When accompanied by specialized tracking algorithms, YOLO achieves the best scores in object tracking. This study focuses on the implementation of YOLO-NAS and YOLO11 in tracking and results demonstrate that YOLO11 is more accurate and stable with BoT-SORT, however, it is faster using ByteTrack method.

Keywords

Single-Object tracking YOLO BoT-SORT ByteTrack SORT DeepSORT

Ethical Statement

Ethics committee approval was not required for this study because there was no study on animals or humans.

References

  • Aharon N, Orfaig R, Bobrovsky BZ. 2022. BoT-SORT: Robust associations multi-pedestrian tracking. arXiv, 2206: 14651.
  • Aharon S, Dupont L, Masad O, Yurkova K, Fridman L, Lkdci, Khvedchenya E, Rubin R, Bagrov N, Tymchenko B, Keren T, Zhilko A, Deci E. 2021. Supergradients. Github Repository, URL: https://github.com/Deci-AI/super-gradients (accessed date: December 4, 2024).
  • Atalı G, Eyüboğlu M. 2022. A study on object detection and tracking of a mobile robot using CIE L*a*b* color space. Düzce Uni J Sci Technol, 10(5): 77-90.
  • Babenko B, Yang MH, Belongie S. 2009. Visual tracking with online multiple instance learning. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, Miami FL, USA, p: 983-990.
  • Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS. 2016. Fully-Convolutional Siamese networks for object tracking. In: Proceedings of Computer Vision–ECCV 2016 Workshops: Proceedings, Springer International Publishing Part II 14, October 8-10 and 15-16, Amsterdam, the Netherlands, p: 850-865.
  • Bewley A, Ge Z, Ott L, Ramos F, Upcroft B. 2016. Simple online and realtime tracking. In: Proceedings of 2016 IEEE International Conference on Image Processing (ICIP), September 20-25, Phoenix AZ, USA, p: 3464-3468.
  • Black MJ, Anandan P. 1993. A framework for the robust estimation of optical flow. In: Proceedings of 1993 4th International Conference on Computer Vision, May 11-14, Berlin, Germany, p: 231-236.
  • Bolme DS, Beveridge JR, Draper BA, Lui YM. 2010. Visual object tracking using adaptive correlation filters. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, San Francisco CA, USA, p: 2544-2550.
  • Bradski GR. 1998. Computer vision face tracking for use in a perceptual user interface. Intel Technol J, 2.
  • Dalal N, Triggs B. 2005. Histograms of oriented gradients for human detection. In: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) 1, June 20-25, San Diego CA, USA, p: 886-893.
  • Du Y, Zhao Z, Song Y, Zhao Y, Su F, Gong T, Meng H. 2023. Strongsort: Make deepsort great again. IEEE Trans Multimedia, 25: 8725-8737.
  • Fan H, Bai H, Lin L, Yang F, Chu P, Deng G, ... Ling H. 2021. LaSOT: A high-quality large-scale single object tracking benchmark. Int J Comput Vis, 129: 439-461.
  • Freund Y, Schapire RE. 1996. Experiments with a new boosting algorithm. In: Proceedings of 13th International Conference on Machine Learning, July 3-6, Bari, Italy, p: 148-156.
  • Fukunaga K, Hostetler LD. 1975. The Estimation of the gradient of a density function, with applications in pattern-recognition. IERE Trans Inf Theory, 21(1): 32-40.
  • Grabner H, Grabner M, Bischof H. 2006. Real-time tracking via on-line boosting. In: Proceedings of the British Machine Vision Conference 2006, British Machine Vision Association BMVA, September 4-7, Edinburg, UK, p: 47–56.
  • Havuç E, Alpak Ş, Çakırel G, Baran MK. 2021. Ping-pong ball tracking through deep learning, Eur J Sci Technol, 27: 629-635.
  • Held D, Thrun S, Savarese S. 2016. Learning to track at 100 fps with deep regression networks. In: Proceedings of Computer Vision–ECCV 2016: 14th European Conference Part I 14 Springer International Publishing, October 11–14, Amsterdam, the Netherlands, p: 749-765.
  • Henriques JF, Caseiro R, Martins P, Batista J. 2014. High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell, 37(3): 583-596.
  • Hu S, Zhao X, Huang K. 2024. SOTVerse: A user-defined task space of single object tracking. Int J Comput Vis, 132(3): 872-930.
  • Jocher G, Qiu J. 2024. Ultralytics YOLO11. URL: https://github.com/ultralytics/ultralytics, (accessed date: December 4, 2024).5
  • Kadam P, Fang G, Zou JJ. 2024. Object tracking using computer vision: A review. Comput, 13(6): 136.
  • Kalal Z, Mikolajczyk K, Matas J. 2010. Forward-backward error: Automatic detection of tracking failures. In: Proceedings of 2010 20th International Conference on Pattern Recognition, August 23–26, Istanbul, Türkiye, p: 2756-2759.
  • Kalal Z, Mikolajczyk K, Matas J. 2012. Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell, 34(7): 1409-1422.
  • Kang, B., Chen, X., Lai, S., Liu, Y., Liu, Y., & Wang, D. 2024. Exploring enhanced contextual information for video-level object tracking. arXiv preprint arXiv:2412.11023.
  • Kim J, Cho J. 2021. A set of single YOLO modalities to detect occluded entities via viewpoint conversion. Appl Sci, 11(13): 6016.
  • Lin L, Fan H, Zhang Z, Wang Y, Xu Y, Ling H. 2025. Tracking meets LoRA: Faster training, larger model, stronger performance. In: Proceedings of European Conference on Computer Vision, Jan 15-16, London, UK, Springer, Cham, p: 300-318.
  • Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL. 2014. Microsoft COCO: Common objects in context. In: Proceedings of Computer Vision–ECCV 2014: 13th European Conference, Proceedings, Part V 13, Springer International Publishing, September 6-12, Zurich, Switzerland, p: 740-755.
  • Lukezic A, Vojir T, Cehovin ZL, Matas J, Kristan M. 2017. Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, Honolulu HI, USA, p: 6309-6318.
  • Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S. 2019. PyTorch: An imperative style, high-performance deep learning library. In: Proceedings of Advances in Neural Information Processing Systems 32, December 8-14, Vancouver, Canada, p: 8026–8037.
  • Redmon J. 2016. You Only Look Once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, Las Vegas NV, USA, p: 779-788.
  • Rossum GV. 2007. Python programming language. In: Proceedings of USENIX Annual Technical Conference 41:1, June 17-22, Santa Clara CA, USA, p: 1-36.
  • Soleimanitaleb Z, Keyvanrad MA. 2022. Single object tracking: A survey of methods, datasets, and evaluation metrics. arXiv preprint arXiv:2201.13066.
  • Şimşek M, Tekbaş MK. 2024. Heatmap creation with YOLO-Deep SORT system customized for in-store customer behavior analysis. Commun Fac Sci Univ Ank Series A2-A3 Phys Sci and Eng, 66(1): 118-131.
  • Tan FG, Yüksel AS, Aydemir E, Ersoy M. 2021. A review on object detection and tracking with deep learning techniques. Eur J Sci Technol, 25: 159-171.
  • Wojke N, Bewley A, Paulus D. 2017. Simple online and realtime tracking with a deep association metric. In: Proceedings of 2017 IEEE International Conference on Image Processing (ICIP), September 17-20, Beijing, China, pp: 3645-3649.
  • Zhang Y, Sun P, Jiang Y, Yu D, Weng F, Yuan Z, Luo P, Liu W, Wang X. 2022. Bytetrack: Multi-object tracking by associating every detection box. In: Proceedings of European Conference on Computer Vision, Cham: Springer Nature Switzerland, October 23–27, Tel Aviv, Israel, p: 1-21.
There are 36 citations in total.

Details

Primary Language English
Subjects Information Systems (Other)
Journal Section Research Articles
Authors

Cevahir Parlak 0000-0002-5500-7379

Publication Date March 15, 2025
Submission Date December 4, 2024
Acceptance Date January 17, 2025
Published in Issue Year 2025 Volume: 8 Issue: 2

Cite

APA Parlak, C. (2025). A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11. Black Sea Journal of Engineering and Science, 8(2), 363-370. https://doi.org/10.34248/bsengineering.1596008
AMA Parlak C. A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11. BSJ Eng. Sci. March 2025;8(2):363-370. doi:10.34248/bsengineering.1596008
Chicago Parlak, Cevahir. “A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11”. Black Sea Journal of Engineering and Science 8, no. 2 (March 2025): 363-70. https://doi.org/10.34248/bsengineering.1596008.
EndNote Parlak C (March 1, 2025) A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11. Black Sea Journal of Engineering and Science 8 2 363–370.
IEEE C. Parlak, “A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11”, BSJ Eng. Sci., vol. 8, no. 2, pp. 363–370, 2025, doi: 10.34248/bsengineering.1596008.
ISNAD Parlak, Cevahir. “A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11”. Black Sea Journal of Engineering and Science 8/2 (March 2025), 363-370. https://doi.org/10.34248/bsengineering.1596008.
JAMA Parlak C. A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11. BSJ Eng. Sci. 2025;8:363–370.
MLA Parlak, Cevahir. “A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11”. Black Sea Journal of Engineering and Science, vol. 8, no. 2, 2025, pp. 363-70, doi:10.34248/bsengineering.1596008.
Vancouver Parlak C. A Comparative Assessment on the Novel Long-Term Real-Time Single Object Tracking Techniques Using Yolo-Nas and YOLO11. BSJ Eng. Sci. 2025;8(2):363-70.
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