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Reducto
- Source :
- SIGCOMM
- Publication Year :
- 2020
- Publisher :
- ACM, 2020.
-
Abstract
- 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.
- Subjects :
- Artificial neural network
Computer science
business.industry
Frame (networking)
Real-time computing
02 engineering and technology
Pipeline (software)
Object detection
Feature (computer vision)
Analytics
020204 information systems
Server
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Enhanced Data Rates for GSM Evolution
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication
- Accession number :
- edsair.doi...........ccb2b059497424e60cd4f8895218b0d5