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A Fast Filtering Mechanism to Improve Efficiency of Large-Scale Video Analytics
- Source :
- IEEE Transactions on Computers. 69:914-928
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Surveillance cameras are ubiquitous around us. Emerging full-feature object-detection models can analyze surveillance videos with high accuracy but consume much computation. Directly applying these models for practical scenarios with large-scale cameras is prohibitively expensive. This, however, is wasteful and unnecessary considering that user-defined anomalies occur rarely among these videos. Therefore, we propose FFS-VA, a multi-stage Fast Filtering Mechanism for Video Analytics, to make video analytics much cost-effective. FFS-VA filters out the frames without the user-defined events by two stream-specialized filters and a cheap full-function model, to reduce the number of frames reaching the full-feature model. FFS-VA presents a global feedback-queue approach to balance the processing speeds of different filters in intra-stream and inter-stream processes. FFS-VA designs a dynamic batch technique to achieve a trade-off between throughput and latency. FFS-VA can also efficiently scale to multiple GPUs. We evaluate FFS-VA against the state-of-the-art YOLOv3 under the same hardware and video workloads. The experimental results show that under a 12.88 percent target-object occurrence rate on two GPUs, FFS-VA can support up to 30 concurrent video streams (15× more than YOLOv3) in the online case, and obtain 10× speedup when offline analyzing a stream, with an accuracy loss of less than 2 percent.
- Subjects :
- Speedup
Scale (ratio)
business.industry
Computer science
Mechanism (biology)
Real-time computing
Latency (audio)
02 engineering and technology
020202 computer hardware & architecture
Theoretical Computer Science
Computational Theory and Mathematics
Hardware and Architecture
Analytics
0202 electrical engineering, electronic engineering, information engineering
business
Throughput (business)
Software
Subjects
Details
- ISSN :
- 23263814 and 00189340
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Computers
- Accession number :
- edsair.doi...........af7f8a4b97efe68d38cb51fa1d5e7526