On Formal Models of Interactions Between Detectors and Trackers in Crowd Analysis Tasks | SpringerLink
Skip to main content
Springer Nature Link
Log in

On Formal Models of Interactions Between Detectors and Trackers in Crowd Analysis Tasks

  • Conference paper
  • First Online:
Computer Vision and Graphics (ICCVG 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 598))

Included in the following conference series:

  • 319 Accesses

Abstract

In crowd analysis tasks (crowds of humans, cattle, birds, drones, etc.) the low-level vision tools are usually the same, i.e. detection and tracking of either individuals or groups. The required results, however, are more complicated (e.g. patterns of group splitting/merging, changes in group sizes and membership, group formation and disappearance, etc.). To complete such tasks, raw results of detection/tracking are converted into data associations representing crowd structure/evolution. Normally, those associations are deterministic and based on target labeling. However, performances of detectors/trackers are non-perfect, i.e. their outcomes are effectively non-deterministic. We discuss matrix-based mathematical models of interactions between detectors and trackers to represent such data associations non-deterministically. In particular, a methodology for reconstructing weak or missing associations by alternative sequences of matrix operations is proposed. This can provide more reliable label correspondences between selected moments/points of monitored scenes. Apart from mathematical details, the paper presents examples illustrating feasibility of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 19447
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 24309
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ali, S., Shah, M.: A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6 (2007). https://doi.org/10.1109/CVPR.2007.382977

  2. Bendali-Braham, M., Weber, J., Forestier, G., Idoumghar, L., Muller, P.A.: Recent trends in crowd analysis: a review. Mach. Learn. Appl. 4, 100,023 (2021). https://doi.org/10.1016/j.mlwa.2021.100023

  3. Bochinski, E., Senst, T., Sikora, T.: Extending iou based multi-object tracking by visual information. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2018). https://doi.org/10.1109/AVSS.2018.8639144

  4. Ciaparrone, G., Luque Sanchez, F., Tabik, S., Troiano, L., Tagliaferri, R., Herrera, F.: Deep learning in video multi-object tracking: a survey. Neurocomputing 381, 61–88 (2020). https://doi.org/10.1016/j.neucom.2019.11.023

    Article  Google Scholar 

  5. Dehghan, A., Modiri Assari, S., Shah, M.: Gmmcp tracker: globally optimal generalized maximum multi clique problem for multiple object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4091–4099 (2015). https://doi.org/10.1109/CVPR.2015.7299036

  6. Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014). https://doi.org/10.1109/TPAMI.2014.2300479

    Article  Google Scholar 

  7. Feldmann, M., Franken, D., Koch, W.: Tracking of extended objects and group targets using random matrices. IEEE Trans. Signal Process. 59(4), 1409–1420 (2011). https://doi.org/10.1109/TSP.2010.2101064

  8. Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets, 3rd edn., chap. Finding Similar Items. Cambridge University Press (2020)

    Google Scholar 

  9. Lia, D., Zhua, J., Xua, B., Lua, M., Li, M.: An ant-based filtering random-finite-set approach to simultaneous localization and mapping. Int. J. Appl. Math. Comput. Sci. 28(3), 505–519 (2018). https://doi.org/10.2478/amcs-2018-0039

    Article  MathSciNet  Google Scholar 

  10. Mazzon, R., Poiesi, F., Cavallaro, A.: Detection and tracking of groups in crowd. In: 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 202–207 (2013). https://doi.org/10.1109/AVSS.2013.6636640

  11. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7263–7271 (2017). https://doi.org/10.1109/CVPR.2017.690

  12. Rezatofighi, S.H., Milan, A., Zhang, Z., Shi, Q., Dick, A., Reid, I.: Joint probabilistic data association revisited. In: IEEE International Conference on Computer Vision (ICCV), pp. 3047–3055. IEEE (2015). https://doi.org/10.1109/ICCV.2015.349

  13. Schranz, M., Umlauft, M., Sende, M., Elmenreich, W.: Swarm robotic behaviors and current applications. Front. Robot. AI 7(36) (2020). https://doi.org/10.3389/frobt.2020.00036

  14. Śluzek, A.: Zastosowanie metod momentowych do identyfikacji obiektów w cyfrowych systemach wizyjnych. Wydawnictwa Politechniki Warszawskiej, Warszawa (1990)

    Google Scholar 

  15. Sreenu, G., Saleem Durai, M.: Intelligent video surveillance: a review through deep learning techniques for crowd analysis. J. Big Data 6(48) (2019). https://doi.org/10.1186/s40537-019-0212-5

  16. Steyer, S., Tanzmeister, G., Lenk, C., Dallabetta, V., Wollherr, D.: Data association for grid-based object tracking using particle labeling. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3036–3043 (2018). https://doi.org/10.1109/ITSC.2018.8569511

  17. Tomar, A., Kumar, S., Pant, B.: Crowd analysis in video surveillance: a review. In: 2022 International Conference on Decision Aid Sciences and Applications (DASA), pp. 162–168 (2022). https://doi.org/10.1109/DASA54658.2022.9765008

  18. Wang, X., Lu, J.: Collective behaviors through social interactions in bird flocks. IEEE Circuits Syst. Mag. 19(3), 6–22 (2019). https://doi.org/10.1109/MCAS.2019.2924507

    Article  Google Scholar 

  19. Zhou, T., Yang, J., Loza, A., Al-Mualla, M., Bhaskar, H.: Crowd modeling framework using fast head detection and shape-aware matching. J. Electron. Imaging 24 (2015). https://doi.org/10.1117/1.JEI.24.2.023019

  20. Zhou, X., Zhuo, J., Krahenbuhl, P.: Bottom-up object detection by grouping extreme and center points. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 850–859 (2019). https://doi.org/10.1109/CVPR.2019.00094

  21. Zitouni, M.S., Bhaskar, H., Al-Mualla, M.E.: Robust background modeling and foreground detection using dynamic textures. In: International Conference on Computer Vision Theory and Applications (VISIGRAPP ’16), pp. 403–410 (2016). https://doi.org/10.5220/0005724204030410

  22. Zitouni, M.S., Śluzek, A.: Video-surveillance tools for monitoring social responsibility under covid-19 restrictions. In: Computer Vision and Graphics (Proceedings of the ICCVG 2020), pp. 227–239. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-59006-2_20

  23. Zitouni, M.S., Śluzek, A.: A data association model for analysis of crowd structure. Int. J. Appl. Math. Comput. Sci. 32(1), 81–94 (2022). https://doi.org/10.34768/amcs-2022-0007

  24. Zitouni, M.S., Sluzek, A., Bhaskar, H.: Cnn-based analysis of crowd structure using automatically annotated training data. In: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (2019). https://doi.org/10.1109/AVSS.2019.8909846

  25. Zitouni, M.S., Sluzek, A., Bhaskar, H.: Visual analysis of socio-cognitive crowd behaviors for surveillance: a survey and categorization of trends and methods. Eng. Appl. Artif. Intell. 82, 294–312 (2019). https://doi.org/10.1016/j.engappai.2019.04.012

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Warsaw University of Life Sciences-SGGW, Warsaw, Poland

    Andrzej Śluzek

  2. Khalifa University, Abu Dhabi, UAE

    M. Sami Zitouni

Authors
  1. Andrzej Śluzek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Sami Zitouni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrzej Śluzek .

Editor information

Editors and Affiliations

  1. Institute of Information Technology, Warsaw University of Life Sciences—SGGW, Warsaw, Poland

    Leszek J. Chmielewski

  2. Institute of Information Technology, Warsaw University of Life Sciences—SGGW, Warsaw, Poland

    Arkadiusz Orłowski

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Śluzek, A., Sami Zitouni, M. (2023). On Formal Models of Interactions Between Detectors and Trackers in Crowd Analysis Tasks. In: Chmielewski, L.J., Orłowski, A. (eds) Computer Vision and Graphics. ICCVG 2022. Lecture Notes in Networks and Systems, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-031-22025-8_2

Download citation

Publish with us

Policies and ethics

Search

Navigation