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Deploying a smart queuing system on edge with Intel OpenVINO toolkit
1Thakur International School Mumbai, Mumbai, India.
Soft Computing
|July 5, 2021
Summary
Edge computing enables smart queuing systems (SQS) on low-cost devices. This AI solution leverages existing hardware, making advanced queue management accessible and cost-effective across various sectors.
Area of Science:
- Computer Vision
- Edge AI
- Machine Learning
Background:
- Advancements in computational power and specialized hardware enable edge machine learning, particularly inference.
- OpenVINO toolkit accelerates the development and deployment of deep learning computer vision applications across diverse hardware.
Purpose of the Study:
- To develop an accessible smart queuing system (SQS) for edge deployments.
- To enable SQS deployment on low-cost, pre-existing hardware, reducing deployment costs.
Main Methods:
- Developed a smart queuing system (SQS) utilizing deep learning algorithms for edge deployment.
- Validated the SQS performance across multiple edge devices: CPU, integrated GPU (iGPU), vision processing unit (VPU), and field-programmable gate arrays (FPGAs).
Main Results:
- Demonstrated the feasibility of creating a video AI solution for smart queuing on the edge.
- Experimental results confirm the promising performance of edge-deployed SQS.
Conclusions:
- Edge AI solutions like SQS can be effectively deployed on affordable, existing hardware.
- This approach significantly reduces the cost and increases the accessibility of intelligent queue management systems.
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