Unmanned Aerial Vehicles for Urban Area Surveillance in Smart Cities
Abstract
The present work presents a tracking application that integrates object detection with a Region-based Convolutional Neural Network as the object detector and the Discriminatory Correlation Filter with Channel and Spatial Reliability as the tracking algorithm for the proposed tracking method. This approach has the objective and motivation to assist the preventive actions of security systems, used in the context of Smart Cities, in urban structures and regions. The generated model results show an average accuracy of 92% for the object tracker when applied to the video sequences of the image dataset.
Keywords:
smart cities, object detection, object tracking, vants, surveillance
References
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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.
Published
2022-07-31
How to Cite
MENEZES, Mathias A. G. de; B., Ricardo Maroquio; MOREIRA, Erick M.; SÁ., Hebert Azevedo; ROSA, Paulo F. F..
Unmanned Aerial Vehicles for Urban Area Surveillance in Smart Cities. In: BRAZILIAN WORKSHOP ON INTELLIGENT CITIES (WBCI), 3. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 95-106.
DOI: https://doi.org/10.5753/wbci.2022.223170.
