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Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics
Open accessAbstract
To cope with the high resource (network and compute) demands of real-time video analytics pipelines, recent systems have relied on frame filtering. However, filtering has typically been done with neural networks running on edge/backend servers that are expensive to operate. This paper investigates on-camera filtering, which moves filtering to the beginning of the pipeline. Unfortunately, we find that commodity cameras have limited compute resources that only permit filtering via frame differencing based on low-level video features. Used incorrectly, such techniques can lead to unacceptable drops in query accuracy. To overcome this, we built Reducto, a system that dynamically adapts filtering decisions according to the time-varying correlation between feature type, filtering threshold, query accuracy, and video content. Experiments with a variety of videos and queries show that Reducto achieves significant (51-97% of frames) filtering benefits, while consistently meeting the desired accuracy.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2015) · doi:10.1109/cvpr31182.2015 · Cited by 3,737
Object Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689
A convolutional neural network cascade for face detection
Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299170 · Cited by 1,390
Deep Convolutional Network Cascade for Facial Point Detection
Sun, Y., Wang, X., & Tang, X. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.446 · Cited by 1,382
A Review of Computer Vision Techniques for the Analysis of Urban Traffic
Buch, N., Velastin, S. A., & Orwell, J. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2119372 · Cited by 653
Metadata
- Authors
- Li, Y., Padmanabhan, A., Zhao, P., Wang, Y., Xu, G. H., & Netravali, R.
- Venue
- Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication
- Publisher
- ACM
- Published
- 2020-07-30
- Pages
- 359-376
- OpenAlex
- W3046256272
- Cited by
- 269
- References
- 56
- FWCI
- 9.65
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Li, Y., Padmanabhan, A., Zhao, P., Wang, Y., Xu, G. H., & Netravali, R. (2020). Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics. Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication. https://doi.org/10.1145/3387514.3405874
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