Reidentificação Veicular em Ambientes Reais com Rotulação Assistida e Dados de Baixa Qualidade
Resumo
Este trabalho traz um sistema de reidentificação veicular com foco em contextos urbanos brasileiros. Propõe-se uma ferramenta open-source para rotulação assistida por redes neurais, que facilita a criação de datasets adaptados à realidade local. Ademais, um sistema de reidentificação espaço-temporal é implementado, usando métricas físicas como distância e velocidade entre câmeras para melhorar a eficiência e precisão. Os resultados mostram que a combinação de datasets locais com benchmarks internacionais, como VeRi-776, melhora a generalização dos modelos (mAP 0,867), destacando a relevância da adaptação regional para sistemas robustos e eficazes em cenários reais.Referências
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Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016). Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP).
Kim, H.-G., Na, Y., Joe, H.-W., Moon, Y.-H., and Cho, Y.-J. (2023). Vehicle re-identification with spatio-temporal information. In 2023 14th International Conference on Information and Communication Technology Convergence (ICTC).
Liao, H., Zheng, S., Shen, X., Li, M. J., and Wang, X. (2022). Semi-automatic data annotation system for multi-target multi-camera vehicle tracking. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA).
Liu, X., Liu, W., Ma, H., and Fu, H. (2016). Large-scale vehicle re-identification in urban surveillance videos.
Liu, X., Liu, W., Zheng, J., Yan, C., and Mei, T. (2020). Beyond the parts: Learning multi-view cross-part correlation for vehicle re-identification. In Proceedings of the 28th ACM International Conference on Multimedia. Association for Computing Machinery.
Lv, K., Du, H., Hou, Y., Deng, W., Sheng, H., Jiao, J., and Zheng, L. (2019). Vehicle re-identification with location and time stamps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
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Wang, Z., Wang, L., Shi, Z., Zhang, M., Geng, Q., and Jiang, N. (2024). A survey on person and vehicle re-identification. IET Computer Vision, 18(8):1235–1268.
Weiser, M. (1999). The computer for the 21st century. SIGMOBILE Mob. Comput. Commun. Rev., 3(3):3–11.
Zhou, Y. and Shao, L. (2018). Viewpoint-aware attentive multi-view inference for vehicle re-identification. In IEEE Conference on Computer Vision and Pattern Recognition.
Amiri, A., Kaya, A., and Keceli, A. S. (2024). A comprehensive survey on deep-learning-based vehicle re-identification: Models, data sets and challenges.
Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016). Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP).
Kim, H.-G., Na, Y., Joe, H.-W., Moon, Y.-H., and Cho, Y.-J. (2023). Vehicle re-identification with spatio-temporal information. In 2023 14th International Conference on Information and Communication Technology Convergence (ICTC).
Liao, H., Zheng, S., Shen, X., Li, M. J., and Wang, X. (2022). Semi-automatic data annotation system for multi-target multi-camera vehicle tracking. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA).
Liu, X., Liu, W., Ma, H., and Fu, H. (2016). Large-scale vehicle re-identification in urban surveillance videos.
Liu, X., Liu, W., Zheng, J., Yan, C., and Mei, T. (2020). Beyond the parts: Learning multi-view cross-part correlation for vehicle re-identification. In Proceedings of the 28th ACM International Conference on Multimedia. Association for Computing Machinery.
Lv, K., Du, H., Hou, Y., Deng, W., Sheng, H., Jiao, J., and Zheng, L. (2019). Vehicle re-identification with location and time stamps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
Sekachev, B. and al., E. (2020). opencv/cvat: v1.1.0.
Tang, Z., Naphade, M., Liu, M.-Y., Yang, X., Birchfield, S., Wang, S., Kumar, R., Anastasiu, D., and Hwang, J.-N. (2019). Cityflow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8789–8798.
Tzutalin (2015). Labelimg. Free Software: MIT License.
Wang, Z., Wang, L., Shi, Z., Zhang, M., Geng, Q., and Jiang, N. (2024). A survey on person and vehicle re-identification. IET Computer Vision, 18(8):1235–1268.
Weiser, M. (1999). The computer for the 21st century. SIGMOBILE Mob. Comput. Commun. Rev., 3(3):3–11.
Zhou, Y. and Shao, L. (2018). Viewpoint-aware attentive multi-view inference for vehicle re-identification. In IEEE Conference on Computer Vision and Pattern Recognition.
Publicado
20/07/2025
Como Citar
SOUZA, Artur Henrique do Nascimento; ABLING, Augusto; VASSALLO, Raquel F..
Reidentificação Veicular em Ambientes Reais com Rotulação Assistida e Dados de Baixa Qualidade. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 17. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 171-180.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2025.9435.
