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Efficient Two-stream Action Recognition on FPGA
- Source :
- CVPR Workshops
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
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Abstract
- Action recognition is an important research field that has many applications in surveillance, video search, autonomous vehicles, etc. However, current state-of-the-art action classifiers are still not widely adopted in embedded applications yet. The major reason is that action recognition needs to process both spatial and temporal streaming data to precisely identify actions, which is compute- intensive and power hungry. To solve this issue, researchers start using FPGA to run action recognition models with minimum power. In this paper, we propose a new hardware architecture of action recognition on FPGA. Our model is based on the popular two-stream neural network. By optimizing the optical flow and convolution operations in the temporal domain, our method can achieve similar accuracy with one order of magnitude less operations than other C3D baseline models. We have implemented our model on Xilinx Ultrascale+ ZCU102 and released the source code.
- Subjects :
- Hardware architecture
Source code
Artificial neural network
business.industry
Computer science
media_common.quotation_subject
Optical flow
Field (computer science)
Domain (software engineering)
Action (philosophy)
Computer engineering
Artificial intelligence
business
Field-programmable gate array
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Accession number :
- edsair.doi...........80f0b7dd931167f3cce2a236b406e23c