A Machine Learning Approach to Interpolating Indoors Trajectories
Resumo
In this article we propose a machine learning-based modeling to solve network overload problems caused by continuous monitoring of the trajectories of multiple tracked devices indoors. The proposed modeling was evaluated with hundreds of object coordinate locations tracked in three synthetic environments and one real environment. We show that it is possible to solve the problem of network overload increasing latency in sending data and predicting as server-side trajectories with ensemble models, such as the Random Forest, and using Artificial Neural Networks. We also show that it is possible to predict at least fifteen intermediate coordinates of the paths of the tracked objects with R2 greater than 0.95.
Palavras-chave:
Data Mining, Internet of Things, Machine Learning
Referências
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Li, G., Geng, E., Ye, Z., Xu, Y., Lin, J., and Pang, Y. Indoor positioning algorithm based on the improved rssi distance model. Sensors 18 (9): 2820, 2018.
Li, X., Liu, Y., Ji, H., Zhang, H., and Leung, V. C. Optimizing resources allocation for fog computing-based internet of things networks. IEEE Access vol. 7, pp. 64907–64922, 2019.
Li, Z., Shi, Y., Wang, C., and Wang, Y. Accurate calibration method for a structured light system. Optical Engineering 47 (5): 053604, 2008.
Liu, S., Liu, C., Luo, Q., Ni, L. M., and Krishnan, R. Calibrating large scale vehicle trajectory data. In 2012 IEEE 13th International Conference on Mobile Data Management. pp. 222–231, 2012.
Mancini, A. Vehicle path prediction for safety enhancement of autonomous driving. Ph.D. thesis, Politecnico di Torino, 2021.
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Miao, H., Cheng, G., Gao, C., Zhang, P., and Yan, Y. Transformer-based online ctc/attention end-to-end speech recognition architecture. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 6084–6088, 2020.
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Ruiz, A. R. J. and Granja, F. S. Comparing ubisense, bespoon, and decawave uwb location systems: Indoor performance analysis. IEEE Transactions on instrumentation and Measurement 66 (8): 2106–2117, 2017.
Samie, F., Bauer, L., and Henkel, J. Iot technologies for embedded computing: A survey. In 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS). IEEE, pp. 1–10, 2016.
Seng, K.-Y., Chen, Y., Chai, K. M. A., Wang, T., Fun, D. C. Y., Teo, Y. S., Tan, P. M. S., Ang, W. H., and Lee, J. K. W. Tracking body core temperature in military thermal environments: An extended kalman filter approach. In 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, pp. 296–299, 2016.
Silva, I. D., Spatti, D. H., and Flauzino, R. A. Redes Neurais Artificiais para engenharia e ciências aplicadas. Arliber Editora Ltda., 2016.
Upchurch, P., Gardner, J., Pleiss, G., Pless, R., Snavely, N., Bala, K., and Weinberger, K. Deep feature interpolation for image content changes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Vikranth, S., Sudheesh, P., and Jayakumar, M. Nonlinear tracking of target submarine using extended kalman filter (ekf). In International Symposium on Security in Computing and Communication. Springer, pp. 258–268, 2016.
Wiest, J., Hoffken, M., Kresel, U., and Dietmayer, K. Probabilistic trajectory prediction with gaussian mixture models. In 2012 IEEE Intelligent Vehicles Symposium. IEEE, 2012.
Wu, Y.-C., Hsu, K.-L., Liu, Y., Hong, C.-Y., Chow, C.-W., Yeh, C.-H., Liao, X.-L., Lin, K.-H., and Chen, Y.-Y. Using linear interpolation to reduce the training samples for regression based visible light positioning system. IEEE Photonics Journal 12 (2): 1–5, 2020.
Akima, H. A new method of interpolation and smooth curve fitting based on local procedures. Journal of the ACM (JACM) 17 (4): 589–602, 1970.
Andreev, S., Galinina, O., Pyattaev, A., Gerasimenko, M., Tirronen, T., Torsner, J., Sachs, J., Dohler, M., and Koucheryavy, Y. Understanding the iot connectivity landscape: a contemporary m2m radio technology roadmap. IEEE Communications Magazine 53 (9): 32–40, 2015.
Bonaccorso, G. Machine learning algorithms. Packt Publishing Ltd, 2017. Breiman, L. Random forests. Machine learning 45 (1): 5–32, 2001.
Campbell, M. Smart edge: The effects of shifting the center of data gravity out of the cloud. Computer 52 (12): 99–102, 2019.
Coronel, P., Furrer, S., Schott, W., and Weiss, B. Indoor location tracking using inertial navigation sensors and radio beacons. In The Internet of Things. Springer, pp. 325–340, 2008.
Faragher, R. and Harle, R. An analysis of the accuracy of bluetooth low energy for indoor positioning applications. In Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014). pp. 201–210, 2014.
Hirakawa, T., Yamashita, T., Tamaki, T., Fujiyoshi, H., Umezu, Y., Takeuchi, I., Matsumoto, S., and Yoda, K. Can ai predict animal movements? filling gaps in animal trajectories using inverse reinforcement learning. Ecosphere 9 (10): e02447, 2018.
Hunter, T., Herring, R., Abbeel, P., and Bayen, A. Path and travel time inference from gps probe vehicle data. NIPS Analyzing Networks and Learning with Graphs 12 (1): 2, 2009.
Joblove, G. H. and Greenberg, D. Color spaces for computer graphics. In Proceedings of the 5th annual conference on Computer graphics and interactive techniques. pp. 20–25, 1978.
Kalman, R. E. A new approach to linear filtering and prediction problems, 1960.
Kennedy, B., Taylor, G. W., and Spachos, P. Ble beacon based patient tracking in smart care facilities. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, pp. 439–441, 2018.
Kennedy, M., Spachos, P., and Taylor, G. W. Ble beacon indoor localization dataset, 2019.
Kucera, E., Haffner, O., and Leskovsky, R. Interactive and virtual/mixed reality applications for mechatronics education developed in unity engine. In 2018 Cybernetics & Informatics (K&I). IEEE, pp. 1–5, 2018.
Lam, C. H., Ng, P. C., and She, J. Improved distance estimation with ble beacon using kalman filter and svm. In 2018 IEEE International Conference on Communications (ICC). IEEE, pp. 1–6, 2018.
Lee, S. K., Bae, M., and Kim, H. Future of iot networks: A survey. Applied Sciences 7 (10): 1072, 2017.
Li, G., Geng, E., Ye, Z., Xu, Y., Lin, J., and Pang, Y. Indoor positioning algorithm based on the improved rssi distance model. Sensors 18 (9): 2820, 2018.
Li, X., Liu, Y., Ji, H., Zhang, H., and Leung, V. C. Optimizing resources allocation for fog computing-based internet of things networks. IEEE Access vol. 7, pp. 64907–64922, 2019.
Li, Z., Shi, Y., Wang, C., and Wang, Y. Accurate calibration method for a structured light system. Optical Engineering 47 (5): 053604, 2008.
Liu, S., Liu, C., Luo, Q., Ni, L. M., and Krishnan, R. Calibrating large scale vehicle trajectory data. In 2012 IEEE 13th International Conference on Mobile Data Management. pp. 222–231, 2012.
Mancini, A. Vehicle path prediction for safety enhancement of autonomous driving. Ph.D. thesis, Politecnico di Torino, 2021.
Meijering, E. A chronology of interpolation: from ancient astronomy to modern signal and image processing. Proceedings of the IEEE 90 (3): 319–342, 2002.
Miao, H., Cheng, G., Gao, C., Zhang, P., and Yan, Y. Transformer-based online ctc/attention end-to-end speech recognition architecture. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 6084–6088, 2020.
Patel, H. A. and Thakore, D. G. Moving object tracking using kalman filter. International Journal of Computer Science and Mobile Computing 2 (4): 326–332, 2013.
Pecher, P., Hunter, M., and Fujimoto, R. Data-driven vehicle trajectory prediction. In Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. SIGSIM-PADS ’16. Association for Computing Machinery, New York, NY, USA, pp. 13–22, 2016.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. Scikit-learn: Machine learning in python. the Journal of machine Learning research vol. 12, pp. 2825–2830, 2011.
Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M., Hoppe, H., and Toyama, K. Digital photography with flash and no-flash image pairs. ACM transactions on graphics (TOG) 23 (3): 664–672, 2004.
Ponte, C., Caminha, C., Bomfim, R., Moreira, R., and Furtado, V. A temporal clustering algorithm for achieving the trade-off between the user experience and the equipment economy in the context of iot. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, pp. 604–609, 2019.
Ponte, C., Caminha, C., and Furtado, V. Otimização de florestas aleatórias através de ponderação de folhas em árvore de regressão. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional. SBC, pp. 698–708, 2020.
Ruiz, A. R. J. and Granja, F. S. Comparing ubisense, bespoon, and decawave uwb location systems: Indoor performance analysis. IEEE Transactions on instrumentation and Measurement 66 (8): 2106–2117, 2017.
Samie, F., Bauer, L., and Henkel, J. Iot technologies for embedded computing: A survey. In 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS). IEEE, pp. 1–10, 2016.
Seng, K.-Y., Chen, Y., Chai, K. M. A., Wang, T., Fun, D. C. Y., Teo, Y. S., Tan, P. M. S., Ang, W. H., and Lee, J. K. W. Tracking body core temperature in military thermal environments: An extended kalman filter approach. In 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, pp. 296–299, 2016.
Silva, I. D., Spatti, D. H., and Flauzino, R. A. Redes Neurais Artificiais para engenharia e ciências aplicadas. Arliber Editora Ltda., 2016.
Upchurch, P., Gardner, J., Pleiss, G., Pless, R., Snavely, N., Bala, K., and Weinberger, K. Deep feature interpolation for image content changes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Vikranth, S., Sudheesh, P., and Jayakumar, M. Nonlinear tracking of target submarine using extended kalman filter (ekf). In International Symposium on Security in Computing and Communication. Springer, pp. 258–268, 2016.
Wiest, J., Hoffken, M., Kresel, U., and Dietmayer, K. Probabilistic trajectory prediction with gaussian mixture models. In 2012 IEEE Intelligent Vehicles Symposium. IEEE, 2012.
Wu, Y.-C., Hsu, K.-L., Liu, Y., Hong, C.-Y., Chow, C.-W., Yeh, C.-H., Liao, X.-L., Lin, K.-H., and Chen, Y.-Y. Using linear interpolation to reduce the training samples for regression based visible light positioning system. IEEE Photonics Journal 12 (2): 1–5, 2020.
Publicado
04/10/2021
Como Citar
CARVALHO, Daniel; SULLIVAN, Daniel; ALMEIDA, Rafael; CAMINHA, Carlos.
A Machine Learning Approach to Interpolating Indoors Trajectories. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 9. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 145-152.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2021.17472.
