The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification
Open accessAbstract
Event-based cameras have recently drawn the attention of the Computer Vision community thanks to their advantages in terms of high temporal resolution, low power consumption and high dynamic range, compared to traditional frame-based cameras. These properties make event-based cameras an ideal choice for autonomous vehicles, robot navigation or UAV vision, among others. However, the accuracy of event-based object classification algorithms, which is of crucial importance for any reliable system working in real-world conditions, is still far behind their frame-based counterparts. Two main reasons for this performance gap are: 1. The lack of effective low-level representations and architectures for event-based object classification and 2. The absence of large real-world event-based datasets. In this paper we address both problems. First, we introduce a novel event-based feature representation together with a new machine learning architecture. Compared to previous approaches, we use local memory units to efficiently leverage past temporal information and build a robust event-based representation. Second, we release the first large real-world event-based dataset for object classification. We compare our method to the state-of-the-art with extensive experiments, showing better classification performance and real-time computation.
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Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Event-based Action Recognition Using Motion Information and Spiking Neural Networks
Liu, Q., Xing, D., Tang, H., Ma, D., & Pan, G. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/240 · Cited by 105
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework ICCAD Special Session Paper
Shafique, M., Marchisio, A., Wicaksana Putra, R. V., & Hanif, M. A. (2021) · 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD) · doi:10.1109/iccad51958.2021.9643539 · Cited by 58
Event-driven Re-Id: A New Benchmark and Method Towards Privacy-Preserving Person Re-Identification
Ahmad, S., Scarpellini, G., Morerio, P., & Bue, A. D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00052 · Cited by 54
Person Re-Identification without Identification via Event Anonymization
Ahmad, S., Morerio, P., & Del Bue, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01022 · Cited by 39
Metadata
- Authors
- Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., & Benosman, R.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1731-1740
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2793704846
- Cited by
- 486
- References
- 67
- FWCI
- 12.23
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., & Benosman, R. (2018). HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00186
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