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2020 | Vol. 21 | 105--114
Tytuł artykułu

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
Języki publikacji
EN
Abstrakty
EN
Embedded platforms with GPU acceleration, designed for performing machine learning on the edge, enabled the creation of inexpensive and pervasive computer vision systems. Smartphones are nowadays widely used for profiling and tracking in marketing, based on WiFi data or beacon-based positioning systems. We present the Arahub system, which aims at integrating world of computer vision systems with smartphone tracking for delivering data useful in interactive applications, such as interactive advertisements. In this paper we present the architecture of the Arahub system and provide insight about its particular elements. Our preliminary results, obtained from real-life test environments and scenarios, show that the Arahub system is able to accurately assign smartphones to their owners, based on visual and WiFi/Bluetooth positioning data. We show the commercial value of such system and its potential applications.
Wydawca

Rocznik
Tom
Strony
105--114
Opis fizyczny
Bibliogr. 37 poz., il.
Twórcy
Bibliografia
  • 1. R. Fraczek, B. Cyganek, and K. Wiatr, “Parallelized algorithms for finding similar images and object recognition,” Computer Science, vol. 14, no. 1, 2013.
  • 2. M. Meina, A. Janusz, K. Rykaczewski, D. Ślęzak, B. Celmer, and A. Krasuski, “Tagging firefighter activities at the emergency scene: Summary of aaia’15 data mining competition at knowledge pit,” in FedCSIS 2015, 2015, pp. 367–373.
  • 3. J. Wilson, S. Chaudhury, and B. Lall, “Clustering short temporal behaviour sequences for customer segmentation using LDA,” Expert Syst. J. Knowl. Eng., vol. 35, no. 3, 2018.
  • 4. I. Rüb, M. Matraszek, P. Konorski, M. Perycz, A. Waśniowski, D. Batorski, and K. Iwanicki, “30 sensors to mars: Toward distributed support systems for astronauts in space habitats,” in ICDCS 2019, 2019, pp. 1704–1714.
  • 5. J. Bulat, K. Duda, M. Duplaga, R. Fraczek, A. Skalski, M. Socha, P. Turcza, and T. P. Zielinski, “Data processing tasks in wireless gi endoscopy: Image-based capsule localization navigation and video compression,” in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 2815–2818.
  • 6. H. Lu and M. A. Cheema, “Indoor data management,” in 2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016, pp. 1414–1417.
  • 7. J. Domaszewicz, S. Lalis, A. Pruszkowski, M. Koutsoubelias, T. Tajmajer, N. Grigoropoulos, M. Nati, and A. Gluhak, “Soft actuation: Smart home and office with human-in-the-loop,” IEEE Pervasive Computing, vol. 15, no. 1, pp. 48–56, 2016.
  • 8. A. Krasuski, A. Jankowski, A. Skowron, and D. Slezak, “From sensory data to decision making: A perspective on supporting a fire commander,” in 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, 2013, pp. 229–236.
  • 9. D. H. Hepting, R. Spring, and D. Ślęzak, “A rough set exploration of facial similarity judgements,” in Transactions on Rough Sets XIV. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 81–99.
  • 10. M. Świechowski and D. Ślęzak, “Introducing logdl - log description language for insights from complex data,” in FedCSIS (in submission), 2020.
  • 11. H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 37, no. 6, pp. 1067–1080, 2007.
  • 12. Q. Dong and W. Dargie, “Evaluation of the reliability of rssi for indoor localization,” in 2012 International Conference on Wireless Communications in Underground and Confined Areas. IEEE, 2012, pp. 1–6.
  • 13. N. Patwari, J. N. Ash, S. Kyperountas, A. O. Hero, R. L. Moses, and N. S. Correal, “Locating the nodes: cooperative localization in wireless sensor networks,” IEEE Signal processing magazine, vol. 22, no. 4, pp. 54–69, 2005.
  • 14. X. Li, K. Pahlavan, M. Latva-aho, and M. Ylianttila, “Comparison of indoor geolocation methods in dsss and ofdm wireless lan systems,” in Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000., vol. 6. IEEE, 2000, pp. 3015–3020.
  • 15. R. Peng and M. L. Sichitiu, “Angle of arrival localization for wireless sensor networks,” in 2006 3rd annual IEEE communications society on sensor and ad hoc communications and networks, vol. 1. Ieee, 2006, pp. 374–382.
  • 16. A. Zhang, Y. Yuan, Q. Wu, S. Zhu, and J. Deng, “Wireless localization based on rssi fingerprint feature vector,” International Journal of Distributed Sensor Networks, vol. 11, no. 11, p. 528747, 2015.
  • 17. A. Golovan, A. A. Panyov, V. V. Kosyanchuk, and A. S. Smirnov, “Efficient localization using different mean offset models in gaussian processes,” in 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2014, pp. 365–374.
  • 18. B. F. D. Hähnel and D. Fox, “Gaussian processes for signal strengthbased location estimation,” in Proceeding of robotics: science and systems. Citeseer, 2006.
  • 19. L. Pei, R. Chen, J. Liu, H. Kuusniemi, T. Tenhunen, and Y. Chen, “Using inquiry-based bluetooth rssi probability distributions for indoor positioning,” Journal of Global Positioning Systems, vol. 9, no. 2, pp. 122–130, 2010.
  • 20. X. Zhao, Z. Xiao, A. Markham, N. Trigoni, and Y. Ren, “Does btle measure up against wifi? a comparison of indoor location performance,” in European Wireless 2014; 20th European Wireless Conference. VDE, 2014, pp. 1–6.
  • 21. T. S. Rappaport et al., Wireless communications: principles and practice. prentice hall PTR New Jersey, 1996, vol. 2.
  • 22. J. S. Kulchandani and K. J. Dangarwala, “Moving object detection: Review of recent research trends,” in 2015 International Conference on Pervasive Computing (ICPC), 2015, pp. 1–5.
  • 23. R. L. F. Fleuret, J. Berclaz and P. Fua, “Multi-camera people tracking with a probabilistic occupancy map,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, p. 267–282, 2008.
  • 24. R. Iguernaissi, D. Merad, K. Aziz, and P. Drap, “People tracking in multi-camera systems: a review,” Multimedia Tools and Applications, vol. 78, 09 2018.
  • 25. Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330–1334, 2000.
  • 26. G. Bradski, “The opencv library. dr. dobb’s journal of software tools,” 2000.
  • 27. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” Lecture Notes in Computer Science, p. 21–37, 2016.
  • 28. D. F. Crouse, “On implementing 2d rectangular assignment algorithms,” IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 4, pp. 1679–1696, 2016.
  • 29. P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, and et. al., “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,” Nature Methods, vol. 17, pp. 261–272, 2020.
  • 30. F. Pedregosa and et. al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
  • 31. B. A. Galler and M. J. Fisher, “An improved equivalence algorithm,” Commun. ACM, vol. 7, no. 5, p. 301–303, May 1964.
  • 32. D. H. Jones, “Book review: Statistical methods, 8th edition george w. snedecor and william g. cochran ames: Iowa state university press, 1989. xix + 491 pp,” Journal of Educational and Behavioral Statistics, vol. 19, no. 3, pp. 304–307, 1994.
  • 33. W. Świeboda, A. Krauze, and H. S. Nguyen, “A granular evacuation modeling framework,” Annals of Computer Science and Information Systems, vol. 2, p. 337–342, 2014.
  • 34. M. Swiechowski and D. Slęzak, “Granular games in real-time environment,” in 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018, pp. 462–469.
  • 35. M. Przyborowski, T. Tajmajer, Grad, A. Janusz, P. Biczyk, and D. Ślęzak, “Toward machine learning on granulated data – a case of compact autoencoder-based representations of satellite images,” in 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 2657–2662.
  • 36. P. Szczuko, “Simple gait parameterization and 3d animation for anonymous visual monitoring based on augmented reality,” Multimedia Tools and Applications, vol. 75, no. 17, pp. 10 561–10 581, Sep 2016.
  • 37. B. Cyganek, “Change detection in multidimensional data streams with efficient tensor subspace model,” in Hybrid Artificial Intelligent Systems. Cham: Springer International Publishing, 2018, pp. 694–705.
Uwagi
1. Track 1: Artificial Intelligence
2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications
3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.baztech-056b18d9-0ca3-4fa7-a6ba-74b6bb338d95
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