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Tracking Methods: Comprehensive Vision and Multiple Approaches

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International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 712))

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Abstract

Tracking of human beings, as well as objects, becomes a great challenge currently presented, it aims to understand the fundamental principles of objects and human beings detected to associate them with robust systems for treaties and draw the desired conclusions. Tracking generally uses several methods such as graph theory, technological tools, Internet of Things (IoT), Big Data, and artificial intelligence, where it maintains several hypotheses to help tracking objects or human beings. In this article, we propose the tracking methods proposed in this direction, and finally, we analyze and discuss the various results obtained.

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References

  1. Leal-Taixé, L., Milan, A., Schindler, K., Cremers, D., Reid, I., Roth, S.: Tracking the trackers: an analysis of the state of the art in multiple object tracking. arXiv preprint arXiv:1704.02781 (2017)

  2. He, F., Deng, Y., Li, W.: Coronavirus disease 2019: what we know? J. Med. Virol. 92(7), 719–725 (2020)

    Article  Google Scholar 

  3. Headey, D.D., Ruel, M.T., et al.: The COVID-19 nutrition crisis: What to expect and how to protect, IFPRI book chapters, pp. 38–41. International Food Policy Research Institute (IFPRI) (2020)

    Google Scholar 

  4. Wu, H., Hu, Y., Wang, K., Li, H., Nie, L., Cheng, H.: Instance-aware representation learning and association for online multi-person tracking. Pattern Recogn. 94, 25–34 (2019)

    Article  Google Scholar 

  5. Wojtusiak, J., Nia, R.M.: Location prediction using GPS trackers: can machine learning help locate the missing people with dementia?. Internet Things, 100035 (2019)

    Google Scholar 

  6. Merad, D., Aziz, K.-E., Iguernaissi, R., Fertil, B., Drap, P.: Tracking multiple persons under partial and global occlusions: application to customers’ behavior analysis. Pattern Recogn. Lett. 81, 11–20 (2016)

    Article  Google Scholar 

  7. M’hand, M.A., Boulmakoul, A., Badir, H., Lbath, A.: A scalable real-time tracking and monitoring architecture for logistics and transport in RoRo terminals. Procedia Comput. Sci. 151, 218–225 (2019)

    Article  Google Scholar 

  8. Benreguia, B., Moumen, H., Merzoug, M.A.: Tracking COVID-19 by tracking infectious trajectories. IEEE Access 8, 145242–145255 (2020)

    Article  Google Scholar 

  9. Ruiz-del-Solar, J., Shats, A., Verschae, R.: Real-time tracking of multiple persons. In: 12th International Conference on Image Analysis and Processing, 2003, Proceedings, pp. 109–114. IEEE (2003)

    Google Scholar 

  10. Thome, N., Merad, D., Miguet, S.: Human body part labeling and tracking using graph matching theory. In: 2006 IEEE International Conference on Video and Signal Based Surveillance, pp. 38–38. IEEE (2006)

    Google Scholar 

  11. Menni, C., et al.: Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat. Med. 26(7), 1037–1040 (2020)

    Article  Google Scholar 

  12. Bollobás, B.: Modern Graph Theory, p. 184. Springer, Heidelberg (2013). https://doi.org/10.1007/978-1-4612-0619-4

    Book  Google Scholar 

  13. Bondy, J.A., Murty, U.S.R.: Théorie des graphes. Springer, Heideleberg (2008)

    Google Scholar 

  14. Buyya, R., Dastjerdi, A.V.: Internet of Things: Principles and Paradigms. Elsevier, Amsterdam (2016)

    Google Scholar 

  15. Norvig, P.R., Intelligence, S.A.: A Modern Approach. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  16. Furht, B., Villanustre, F.: Big Data Technologies and Applications. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44550-2

    Book  Google Scholar 

  17. Al Rahhal, M.M., Bazi, Y., Abdullah, T., Mekhalfi, M.L., AlHichri, H., Zuair, M.: Learning a multi-branch neural network from multiple sources for knowledge adaptation in remote sensing imagery. Remote Sens. 10(12), 1890 (2018)

    Article  Google Scholar 

  18. Han, M., Sethi, A., Hua, W., Gong, Y.: A detection-based multiple object tracking method. In: 2004 International Conference on Image Processing, 2004, ICIP 2004, vol. 5, pp. 3065–3068. IEEE (2004)

    Google Scholar 

  19. Berry, M.W., Mohamed, A., Yap, B.W. (eds.): Supervised and Unsupervised Learning for Data Science. USL, Springer, Cham (2020). https://doi.org/10.1007/978-3-030-22475-2

    Book  Google Scholar 

  20. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, vol. 1, 2nd edn. MIT press, Cambridge (2016)

    MATH  Google Scholar 

  21. McKenna, S.J., Jabri, S., Duric, Z., Rosenfeld, A., Wechsler, H.: Tracking groups of people. Comput. Vision Image Underst. 80(1), 42–56 (2000)

    Article  MATH  Google Scholar 

  22. Lee, H.C., Luong, D.T., Cho, C.W., Lee, E.C., Park, K.R.: Gaze tracking system at a distance for controlling IPTV. IEEE Trans. Cons. Electron. 56(4), 2577–2583 (2010)

    Article  Google Scholar 

  23. Celebi, M.E., Aydin, K. (eds.): Unsupervised Learning Algorithms. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-24211-8

    Book  Google Scholar 

  24. Huo, F., Hendriks, E.A.: Multiple people tracking and pose estimation with occlusion estimation. Comput. Vision Image Underst. 116(5), 634–647 (2012)

    Article  Google Scholar 

  25. Wengefeld, T., Lewandowski, B., Seichter, D., Pfennig, L., Müller, S., Gross, H.M.: Real-time person orientation estimation and tracking using colored point clouds. Rob. Auton. Syst. 135, 103665 (2021)

    Article  Google Scholar 

  26. Küçükkeçeci, C., Yazici, A.: Multilevel object tracking in wireless multimedia sensor networks for surveillance applications using graph-based big data. IEEE Access 7, 67818–67832 (2019)

    Article  Google Scholar 

  27. Lukasczyk, J., Weber, G., Maciejewski, R., Garth, C., Leitte, H.: Nested tracking graphs. Comput. Graph. Forum 36(3), 12–22 (2017)

    Google Scholar 

  28. Xiao, C., et al.: A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls. Comput. Med. Imaging Graphi. 87, 101819 (2021)

    Article  Google Scholar 

  29. Widanagamaachchi, W., Christensen, C., Pascucci, V., Bremer, P.-T.: Interactive exploration of large-scale time-varying data using dynamic tracking graphs. In: IEEE Symposium on Large Data Analysis and Visualization (LDAV), pp. 9–17. IEEE (2012)

    Google Scholar 

  30. Meijering, E., Dzyubachyk, O., Smal, I.: Methods for cell and particle tracking. Methods Enzymol. 504, 183–200 (2012)

    Article  Google Scholar 

  31. Karimov, K.S., Saqib, M.A., Akhter, P., Ahmed, M.M., Chattha, J.A., Yousafzai, S.A.: A simple photo-voltaic tracking system. Solar Energy Mater. Solar Cells 87(1–4), 49–59 (2005)

    Article  Google Scholar 

  32. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 13-ES (2006)

    Article  Google Scholar 

  33. Walter, T., Couzin, I.D.: TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields. Elife 10, e64000 (2021)

    Article  Google Scholar 

  34. Tryggvason, G., et al.: A front-tracking method for the computations of multiphase flow. J. Comput. Phys. 169(2), 708–759 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  35. Metcalf, C.E., Kemper, P., Kohn, L.T., Pickreign, J.D.: Site definition and sample design for the Community Tracking Study. Center for Studying Health System Change, Washington, DC (1996)

    Google Scholar 

  36. Peng, J., et al.: TPM: Multiple object tracking with tracklet-plane matching. Pattern Recogn. 107, 107480 (2020)

    Article  Google Scholar 

  37. Chenouard, N., et al.: Objective comparison of particle tracking methods. Nat. Methods 11(3), 281–289 (2014)

    Article  Google Scholar 

  38. Scopus preview - Scopus - Welcome to Scopus. https://www.scopus.com/. Accessed 27 June 2021

  39. Liu, Q., et al.: Online multi-object tracking with unsupervised re-identification learning and occlusion estimation. Neurocomputing 483, 333–347 (2022)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Intelligent Processing Systems and Security Team, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco

    Anass Ariss, Imane Ennejjai, Nassim Kharmoum & Soumia Ziti

  2. National Center for Scientific and Technical Research (CNRST), Rabat, Morocco

    Nassim Kharmoum

  3. E2SN Research Team, ENSAM Rabat, Mohammed V University in Rabat, Rabat, Morocco

    Wajih Rhalem

  4. Mathematics and Applications Laboratory, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaadi University, Tétouan, Morocco

    Mostafa Ezziyyani

Authors
  1. Anass Ariss
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  2. Imane Ennejjai
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  3. Nassim Kharmoum
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  4. Wajih Rhalem
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  5. Soumia Ziti
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  6. Mostafa Ezziyyani
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Corresponding author

Correspondence to Anass Ariss .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  2. Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Ariss, A., Ennejjai, I., Kharmoum, N., Rhalem, W., Ziti, S., Ezziyyani, M. (2023). Tracking Methods: Comprehensive Vision and Multiple Approaches. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 712. Springer, Cham. https://doi.org/10.1007/978-3-031-35251-5_5

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