Veículos Aéreos Não Tripulados para a Vigilância de Áreas Urbanas em Cidades Inteligentes
Resumo
Esta pesquisa apresenta uma aplicação de rastreamento que integra a detecção de objetos com uma Rede Neural Convolucional baseada em Região como detector de alvos de interesse e o Filtro de Correlação Discriminativa com Canal e Confiabilidade Espacial como algoritmo de rastreamento para o método proposto. Esta abordagem tem o objetivo e a motivação de auxiliar as ações preventivas de sistemas de segurança, empregados no contexto de Cidades Inteligentes, em estruturas e regiões urbanas. Os resultados do modelo gerado mostram uma precisão média de 92% para o rastreador de objetos quando aplicado às sequências de vídeo do conjunto de imagens.
Palavras-chave:
cidades inteligentes, detecção de objetos, rastreamento de objetos, vants, vigilância
Referências
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Uijlings, J., Sande, K., Gevers, T., and Smeulders, A. (2013). Selective search for object recognition. International Journal of Computer Vision, 104:154–171.
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Baldoni, G., Melita, M., Micalizzi, S., Rametta, C., Schembra, G., and Vassallo, A. (2017). A dynamic, plug-and-play and efficient video surveillance platform for smart cities. In 2017 14th IEEE Annual Consumer Communications Networking Conference (CCNC), pages 611–612.
Daikoku, M., Karungaru, S., and Terada, K. (2013). Automatic detection of suspicious objects using surveillance cameras. In The SICE Annual Conference 2013, pages 1162–1167.
Dange, A. D. and Momin, B. F. (2019). The cnn and dpm based approach for multiple object detection in images. In 2019 International Conference on Intelligent Computing and Control Systems (ICCS), pages 1106–1109.
Dilshad, N., Hwang, J., Song, J., and Sung, N. (2020). Applications and challenges in video surveillance via drone: A brief survey. In 2020 International Conference on Information and Communication Technology Convergence (ICTC), pages 728–732.
Geng, H., Guan, J., Pan, H., and Fu, H. (2018). Multiple vehicle detection with different scales in urban surveillance video. In 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM), pages 1–4.
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2013). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 580–587.
Hu, C., Qu, G., Shin, H.-S., and Tsourdos, A. (2021). Distributed synchronous coopeInternational rative tracking algorithm for ground moving target in urban by uavs. Journal of Systems Science, 52(4):832–847.
Jin, Y., Qian, Z., and Yang, W. (2020). Uav cluster-based video surveillance system optimization in heterogeneous communication of smart cities. IEEE Access, 8:55654– 55664.
Khan, S., Teng, Y., and Cui, J. (2021). Pedestrian traffic lights classification using transfer learning in smart city application. In 2021 13th International Conference on Communication Software and Networks (ICCSN), pages 352–356.
Li, J., Wong, H.-C., Lo, S.-L., and Xin, Y. (2018). Multiple object detection by a deformable part-based model and an r-cnn. IEEE Signal Processing Letters, 25(2):288–292.
Mao, R. (2021). Real-time small-size pixel target perception algorithm based on embedded system for smart city. In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), pages 505–511.
Pramanik, A., Pal, S. K., Maiti, J., and Mitra, P. (2022). Granulated rcnn and multi-class IEEE Transactions on Emerging deep sort for multi-object detection and tracking. Topics in Computational Intelligence, 6(1):171–181.
Samad, T., Bay, J. S., and Godbole, D. (2007). Network-centric systems for military operations in urban terrain: The role of uavs. Proceedings of the IEEE, 95(1):92–107.
Samant, A. and Chang, K. (2010). Image-based tracking and sensor resource management for uavs in an urban environment. Proceedings of SPIE The International Society for Optical Engineering.
Semsch, E., Jakob, M., Pavlicek, D., and Pechoucek, M. (2009). Autonomous uav surveillance in complex urban environments. In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, volume 2, pages 82–85, Milan, Italy.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556.
Thakur, D. N., Nagrath, P., Jain, R., Saini, D., Sharma, N., and D, J. (2021). Artificial Intelligence Techniques in Smart Cities Surveillance Using UAVs: A Survey, pages 329–353.
Uijlings, J., Sande, K., Gevers, T., and Smeulders, A. (2013). Selective search for object recognition. International Journal of Computer Vision, 104:154–171.
Utomo, W., Bhaskara, P. W., Kurniawan, A., Juniastuti, S., and Yuniarno, E. M. (2020).
Traffic congestion detection using fixed-wing unmanned aerial vehicle (uav) video streaming based on deep learning. In 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), pages 234–238.
Wan, S., Lu, J., Fan, P., and Letaief, K. B. (2018). To smart city: Public safety network design for emergency. IEEE Access, 6:1451–1460.
Publicado
31/07/2022
Como Citar
MENEZES, Mathias A. G. de; B., Ricardo Maroquio; MOREIRA, Erick M.; SÁ., Hebert Azevedo; ROSA, Paulo F. F..
Veículos Aéreos Não Tripulados para a Vigilância de Áreas Urbanas em Cidades Inteligentes. In: WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI), 3. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 95-106.
DOI: https://doi.org/10.5753/wbci.2022.223170.
