Sistema de Detecção de Usuários Vulneráveis da Via Robusto à Baixa Iluminação
Resumo
Neste trabalho, é proposto um sistema ADAS baseado em visão computacional para aumentar a segurança de Usuários Vulneráveis das Vias (VRUs) em regiões de ponto cego, utilizando exclusivamente imagens de câmeras traseiras de veículos. O método desenvolvido integra detecção e rastreamento multiobjetos para emitir alertas de proximidade de forma eficiente. Além disso, o sistema incorpora um mecanismo auxiliar para condições de baixa luminosidade, baseado na detecção de faróis. Para apoiar a pesquisa nessa área, é apresentado o FilterLane-VRU, um novo conjunto de dados com cenas reais de tráfego urbano capturadas por câmeras traseiras e anotadas com rótulos de alerta. O pipeline proposto oferece uma solução confiável e de baixo custo para aumentar a segurança dos VRUs em ambientes urbanos.
Palavras-chave:
Sistema ADAS, Usuários vulneráveis da via (VRU), Visão computacional, Detecção e rastreamento de objetos
Referências
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Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779–788).
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28.
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Van Beeck, K., & Goedemé, T. (2016). The automatic blind spot camera: A vision-based active alarm system. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part I 14 (pp. 122–135). Springer.
Wang, Z., Huang, Z., Gao, Y., Wang, N., & Liu, S. (2024). MV2DFusion: Leveraging modality-specific object semantics for multi-modal 3D detection. arXiv preprint arXiv:2408.05945.
Wojke, N., Bewley, A., & Paulus, D. (2017). Simple online and real-time tracking with a deep association metric. In 2017 IEEE International Conference on Image Processing (ICIP) (pp. 3645–3649).
Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018). SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1887–1893).
Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., & Wang, X. (2022). ByteTrack: Multi-object tracking by associating every detection box. In European Conference on Computer Vision (pp. 1–21).
Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., & Chen, J. (2024). DETRs beat YOLOs on real-time object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 16965–16974).
Alaqeel, A. A., Alburadi, A., Nashashibi, A. Y., Sarabandi, K., & Shaman, H. (2023). Detection and identification of pedestrians and bicyclists using J-band automotive radars. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 6157–6159).
Borba, T. D., Vaculín, O., Marzbani, H., & Jazar, R. N. (2025). Increasing safety of vulnerable road users in scenarios with occlusion: A collaborative approach for smart infrastructures and automated vehicles. IEEE Access, 13, 8851–8885.
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020). NuScenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11621–11631).
de Carvalho, C. H. R., & Guedes, E. P. (2023). Balanço da primeira década de ação pela segurança no trânsito no Brasil e perspectivas para a segunda década. Administração Pública. Governo. Estado.
Dubey, A., Fuchs, J., Reissland, T., Weigel, R., & Lurz, F. (2020). Uncertainty analysis of deep neural network for classification of vulnerable road users using micro-Doppler. In 2020 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT) (pp. 23–26).
Han, L., Zheng, P., Li, H., Chen, J., Hua, Z., & Zhang, Z. (2022). A novel early warning strategy for right-turning blind zone based on vulnerable road users detection. Neural Computing and Applications, 1–20.
Jocher, G., Qiu, J., & Chaurasia, A. (2023). Ultralytics YOLO.
Liu, Z., Tang, H., Amini, A., Yang, X., Mao, H., Rus, D. L., & Han, S. (2023). BevFusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2774–2781).
Malinverno, M., Avino, G., Casetti, C., Chiasserini, C.-F., Malandrino, F., & Scarpina, S. (2018). Performance analysis of C-V2I-based automotive collision avoidance. In 2018 IEEE 19th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM) (pp. 1–9).
Mannion, P. (2019). Vulnerable road user detection: State-of-the-art and open challenges. arXiv preprint arXiv:1902.03601.
Ortiz, F. M., Sammarco, M., Costa, L. H. M. K., & Detyniecki, M. (2023). Applications and services using vehicular exteroceptive sensors: A survey. IEEE Transactions on Intelligent Vehicles, 8(1), 949–969.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779–788).
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28.
Silva, R. M., Azevedo, G. F., Berto, M. V. V., Rocha, J. R., Fidelis, E. C., Nogueira, M. V., Lisboa, P. H., & Almeida, T. A. (2024). Vulnerable road user detection and safety enhancement: A comprehensive survey.
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2446–2454).
Van Beeck, K., & Goedemé, T. (2016). The automatic blind spot camera: A vision-based active alarm system. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part I 14 (pp. 122–135). Springer.
Wang, Z., Huang, Z., Gao, Y., Wang, N., & Liu, S. (2024). MV2DFusion: Leveraging modality-specific object semantics for multi-modal 3D detection. arXiv preprint arXiv:2408.05945.
Wojke, N., Bewley, A., & Paulus, D. (2017). Simple online and real-time tracking with a deep association metric. In 2017 IEEE International Conference on Image Processing (ICIP) (pp. 3645–3649).
Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018). SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1887–1893).
Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., & Wang, X. (2022). ByteTrack: Multi-object tracking by associating every detection box. In European Conference on Computer Vision (pp. 1–21).
Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., & Chen, J. (2024). DETRs beat YOLOs on real-time object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 16965–16974).
Publicado
19/05/2025
Como Citar
AVENA, Vinicius; COUTO, Rodrigo S.; CAMPISTA, Miguel Elias M.; COSTA, Luís Henrique M. K..
Sistema de Detecção de Usuários Vulneráveis da Via Robusto à Baixa Iluminação. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 9. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 155-168.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2025.9064.
