Semantic Scene Filtering for Event Cameras in Long-Term Outdoor Monitoring Scenarios
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47966-3_7
Reference15 articles.
1. Alonso, I., Murillo, A.C.: EV-SegNet: semantic segmentation for event-based cameras. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1624–1633 (2019). https://doi.org/10.1109/CVPRW.2019.00205
2. Baldwin, R.W., Almatrafi, M., Asari, V., Hirakawa, K.: Event probability mask (EPM) and event denoising convolutional neural network (EDnCNN) for neuromorphic cameras. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020). https://doi.org/10.1109/CVPR42600.2020.00177
3. Bolten, T., Lentzen, F., Pohle-Fröhlich, R., Tönnies, K.: Evaluation of deep learning based 3D-point-cloud processing techniques for semantic segmentation of neuromorphic vision sensor event-streams. In: Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, pp. 168–179. INSTICC, SciTePress (2022). https://doi.org/10.5220/0010864700003124
4. Bolten, T., Pohle-Fröhlich, R., Tönnies, K.D.: DVS-OUTLAB: a neuromorphic event-based long time monitoring dataset for real-world outdoor scenarios. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1348–1357 (2021). https://doi.org/10.1109/CVPRW53098.2021.00149
5. Chen, G., et al.: Multi-cue event information fusion for pedestrian detection with neuromorphic vision sensors. Front. Neurorobot. 13, 10 (2019). https://doi.org/10.3389/fnbot.2019.00010
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