Person Re-Identification without Identification via Event Anonymization
Open accessAbstract
Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vision sensors (event-cameras) have been recently considered a valid solution to the privacy issue because they do not capture detailed RGB visual information of the subjects in the scene. However, recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity, reintroducing a potential threat to privacy for event-based vision applications. In this paper, we aim to anonymize event-streams to protect the identity of human subjects against such image reconstruction attacks. To achieve this, we propose an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person ReId. Our network learns to scramble events, enforcing the degradation of images recovered from the privacy attacker. In this work, we also bring to the community the first ever event-based person ReId dataset gathered to evaluate the performance of our approach. We validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available SoftBio dataset and our proposed Event-ReId dataset. The code is available at https://github.com/IIT-PAVIS/ReId_without_Id
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Using Event-ReId-V2 dataset increases rank-1 accuracy by 13.5% and mAP by 11.3% compared to Event-ReId-V1.
“Table 3 shows an increase of 13.5 % in rank1 accuracy and 11.3 % in mAP using the Event-ReId-V2 dataset.”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [15]
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- Authors
- Ahmad, S., Morerio, P., & Del Bue, A.
- Affiliations
- Italian Institute of Technology (IT)
- Pattern Analysis & Computer Vision (PAVIS) - Istituto Italino di Tecnologia,Italy
- ORCID
- Morerio, Pietro
- Venue
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2023-10-01
- Pages
- 11098-11107
- OpenAlex
- W4390873006
- Cited by
- 39
- References
- 44
- Citations by year
- 2026: 12 · 2025: 16 · 2024: 10 · 2023: 1
- FWCI
- 4.18
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- European Commission
- NextGenerationEU
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- person re-identification, event cameras, privacy preservation, visual surveillance, neuromorphic vision
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Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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