The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.
Direct face detection and video reconstruction from event cameras
Abstract
Event cameras are emerging as a new class of cameras, to potentially rival conventional CMOS cameras, because of their high speed operation and low power consumption. Pixels in an event camera operate in parallel and fire asynchronous spikes when individual pixels encounter a change in intensity that is greater than a pre-determined threshold. Such event-based cameras have an immense potential in battery-operated or always-on application scenarios, owing to their low power consumption. These event-based cameras can be used for direct detection from event streams, and we demonstrate this potential using face detection as an example application. We first propose and develop a patch-based model for the event streams acquired from such cameras. We demonstrate the utility and robustness of the patch-based model for event-based video reconstruction and event-based direct face detection. We are able to reconstruct images and videos at over 2,000 fps from the acquired event streams. In addition, we demonstrate the first direct face detection from event streams, highlighting the potential of these event-based cameras for power-efficient vision applications.
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“Recently, deep neural network-based event-to-image conversion models [8]-[11] have demonstrated impressive abilities in recovering gray-scale images from event streams, posing a potential risk to the privacy aspect of event-based vision applications.”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [36]
Citations
Cites
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Rapid object detection using a boosted cascade of simple features
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Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Aharon, M., Elad, M., & Bruckstein, A. M. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881199 · Cited by 9,464
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
Image Super-Resolution Via Sparse Representation
Yang, J., Wright, J. N., Huang, T. S., & Ma, Y. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2050625 · Cited by 5,241
Pati, Y., Rezaiifar, R., & Krishnaprasad, P. (1993) · Proceedings of 27th Asilomar Conference on Signals, Systems and Computers · doi:10.1109/acssc.1993.342465 · Cited by 4,928
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
Lichtsteiner, P., Posch, C., & Delbruck, T. (2008) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2007.914337 · Cited by 2,485 · cited 13×
“Data Finally, we conduct face detection experiments using real event streams from the DVS128.”
BackgroundMethodInfluentialFace detection, pose estimation, and landmark localization in the wild
Zhu, X., & Ramanan, D. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248014 · Cited by 2,171
Research works printed but not linked: 3
- Grasshopper3 4.1 mp mono usb3 vision (cmosis cmv40003e5)
- 10.1016/j.neunet.2013.03.006 · DOI
- 2014, MATLAB. version 8.3 (R2014a)
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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
Gallego, G., Delbrück, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.3929/ethz-b-000521357 · Cited by 590
Events-To-Video: Bringing Modern Computer Vision to Event Cameras
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00398 · Cited by 462
“contrast to [4], we do not reconstruct individual intensity images from small windows of events, but instead reconstruct a temporally consistent video from a long stream of events (several seconds) using a recurrent network.”
BackgroundBringing a Blurry Frame Alive at High Frame-Rate With an Event Camera
Pan, L., Scheerlinck, C., Yu, X., Hartley, R. I., Liu, M., & Dai, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00698 · Cited by 318
“The existing methods can be in general categorized as 1) Event data only solutions [1, 27, 2], where the results tend to lack the texture and consistency of natural videos, as they fail to use the complementary information contained in the low frame-rate intensity image; 2) Low framerate intensity-image-only solutions…”
MethodFast Image Reconstruction with an Event Camera
Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N. M., Mahony, R. E., & Scaramuzza, D. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093366 · Cited by 252
“[4], who used a simulator to learn a sparse patch-based dictionary to match event patches to gradient patches.”
Metadata
- Authors
- Miyatani, Y., Barua, S., & Veeraraghavan, A.
- Affiliations
- Rice University (US)
- Venue
- 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2016-03
- Volume
- 2016
- Pages
- 1-9
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2403865344
- Cited by
- 133
- References
- 37
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 19 · 2023: 18 · 2022: 8 · 2021: 20 · 2020: 21 · 2019: 18 · 2018: 3 · 2017: 2 · 2016: 2
- FWCI
- 2.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- IEEE Conference Proceedings
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- event cameras, event-based vision, face detection, video reconstruction, high-speed imaging, low-power vision, event stream processing
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