Otimização de Sistemas IoMT Utilizando Sumarização de Vídeo em Tempo Real como Ferramenta de Inteligência em Dispositivos de Borda
Resumo
O avanço dos dispositivos conectados aumentou a geração de dados, especialmente com a demanda por aplicativos multimídia em tempo real em sistemas para Internet das Coisas Multimídia (IoMT). A transmissão de grandes quantidades de dados multimídia, como vídeos, pode sobrecarregar a infraestrutura e causar falhas de desempenho. Este trabalho propõe uma técnica de sumarização de vídeo utilizando computação nas bordas para aprimorar sistemas IoMT. Os resultados apresentam uma redução na sobrecarga da rede, mantendo o balanceamento entre os recursos computacionais e de rede.Referências
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Hussain, T., Muhammad, K., Ding, W., Lloret, J., Baik, S. W., and de Albuquerque, V. H. C. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognit., 109(107567):107567.
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Nauman, A., Qadri, Y. A., Amjad, M., Zikria, Y. B., Afzal, M. K., and Kim, S. W. (2020). Multimedia internet of things: A comprehensive survey. Ieee Access, 8:8202–8250.
Ou, S.-H., Lee, C.-H., Somayazulu, V. S., Chen, Y.-K., and Chien, S.-Y. (2014). On-line multi-view video summarization for wireless video sensor network. IEEE Journal of Selected Topics in Signal Processing, 9(1):165–179.
Redmon, J., Divvala, S. K., Girshick, R. B., and Farhadi, A. (2015). You only look once: Unified, real-time object detection. CoRR, abs/1506.02640.
Sabah, A. and Selwal, A. (2023). Data-driven enabled approaches for criteria-based video summarization: a comprehensive survey, taxonomy, and future directions. Multimedia Tools and Applications, 82:32635–32709.
Shambharkar, P. G. and Goel, R. (2022). From video summarization to real time video summarization in smart cities and beyond: A survey. Front. Big Data, 5:1106776.
Uke, S., Junghare, P., Kenjale, S., Korade, S., and Kothwade, A. (2024). Comprehensive real-time intrusion detection system using iot, computer vision (opencv), and machine learning (yolo) algorithms. In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), pages 1680–1689.
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Zhang, K., Chao, W.-L., Sha, F., and Grauman, K. (2016). Video summarization with long short-term memory. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII 14, pages 766–782. Springer.
Alvi, S. A., Afzal, B., Shah, G. A., Atzori, L., and Mahmood, W. (2015). Internet of multimedia things: Vision and challenges. Ad Hoc Networks, 33:87–111.
Battisti, A. L. É., Muchaluat-Saade, D. C., and Delicato, F. C. (2021). Enabling internet of media things with edge-based virtual multimedia sensors. IEEE Access, 9:59255–59269.
Bendraou, Y., Essannouni, F., and Salam, A. (2019). From local to global key-frame extraction based on important scenes using svd of centrist features. Multimedia Tools and Applications, 78(2):1441–1456.
Bhering, F., Passos, D., Ochi, L. S., Obraczka, K., and Albuquerque, C. (2022). Wireless multipath video transmission: when iot video applications meet networking—a survey. Multimedia Systems, 28(3):831–850.
del Blanco, C. R., Carballeira, P., Jaureguizar, F., and García, N. (2021). Robust people indoor localization with omnidirectional cameras using a grid of Spatial-Aware classifiers. Signal Process. Image Commun., 93(116135):116135.
Dilawari, A. and Khan, M. U. G. (2019). ASoVS: Abstractive summarization of video sequences. IEEE Access, 7:29253–29263.
Ericsson (2024). Ericsson mobility report. Technical report, Ericsson. Accessed: YYYY-MM-DD.
Hussain, T., Muhammad, K., Del Ser, J., Baik, S. W., and de Albuquerque, V. H. C. (2019). Intelligent embedded vision for summarization of multiview videos in iiot. IEEE Transactions on Industrial Informatics, 16(4):2592–2602.
Hussain, T., Muhammad, K., Ding, W., Lloret, J., Baik, S. W., and de Albuquerque, V. H. C. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognit., 109(107567):107567.
Ji, Z., Xiong, K., Pang, Y., and Li, X. (2020). Video summarization with attention-based encoder–decoder networks. IEEE Trans. Circuits Syst. Video Technol., 30(6):1709–1717.
Jocher, G. and Qiu, J. (2024). Ultralytics yolo11.
Lai, L., Suda, N., and Chandra, V. (2018). CMSIS-NN: efficient neural network kernels for arm cortex-m cpus. CoRR, abs/1801.06601.
Meena, P., Kumar, H., and Kumar Yadav, S. (2023). A review on video summarization techniques. Eng. Appl. Artif. Intell., 118(105667):105667.
Nauman, A., Qadri, Y. A., Amjad, M., Zikria, Y. B., Afzal, M. K., and Kim, S. W. (2020). Multimedia internet of things: A comprehensive survey. Ieee Access, 8:8202–8250.
Ou, S.-H., Lee, C.-H., Somayazulu, V. S., Chen, Y.-K., and Chien, S.-Y. (2014). On-line multi-view video summarization for wireless video sensor network. IEEE Journal of Selected Topics in Signal Processing, 9(1):165–179.
Redmon, J., Divvala, S. K., Girshick, R. B., and Farhadi, A. (2015). You only look once: Unified, real-time object detection. CoRR, abs/1506.02640.
Sabah, A. and Selwal, A. (2023). Data-driven enabled approaches for criteria-based video summarization: a comprehensive survey, taxonomy, and future directions. Multimedia Tools and Applications, 82:32635–32709.
Shambharkar, P. G. and Goel, R. (2022). From video summarization to real time video summarization in smart cities and beyond: A survey. Front. Big Data, 5:1106776.
Uke, S., Junghare, P., Kenjale, S., Korade, S., and Kothwade, A. (2024). Comprehensive real-time intrusion detection system using iot, computer vision (opencv), and machine learning (yolo) algorithms. In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), pages 1680–1689.
Wang, X., Magno, M., Cavigelli, L., and Benini, L. (2019). Fann-on-mcu: An open-source toolkit for energy-efficient neural network inference at the edge of the internet of things. CoRR, abs/1911.03314.
Zhang, K., Chao, W.-L., Sha, F., and Grauman, K. (2016). Video summarization with long short-term memory. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII 14, pages 766–782. Springer.
Publicado
19/07/2026
Como Citar
ALVES, Arthur Mendes Rocha; BHERING, Fabiano Pereira.
Otimização de Sistemas IoMT Utilizando Sumarização de Vídeo em Tempo Real como Ferramenta de Inteligência em Dispositivos de Borda. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 18. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 339-349.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2026.23219.
