Smart Queue Monitoring System - Project

Details
Programming Language:
Intel OpenVINO ToolKit:
Docker (Ubuntu OpenVINO pre-installed):	mmphego/intel-openvino
Hardware Used:	Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz
Device:	CPU
Blog Post
Visitors

This project demonstrates how to detect people in queues (in order to redirect them to shortest queue) using inference on pre-trained neural network with Intel OpenVINO framework.

The purpose of this project is to choose the right hardware suitable for a particular scenario. See Scenarios.md

Main Tasks

The following pages will walk you through the steps of the project. At a high level, you will:

Propose a possible hardware solution
Build out your application and test its performance on the DevCloud using multiple hardware types
Compare the performance to see which hardware performed best
Revise your proposal based on the test results

Proposal Submission per Scenario

Scenario 1: Manufacturing
- FPGA

Scenario 2: Retail Sector
- CPU and IGPU

Scenario 3: Transportation
- VPU

Results

The application was tested on a number of hardware and the results can be accessed here.

Requirements

Hardware

CPU/IGPU: i5-i7 Intel® Core™ processor with Iris® Pro graphics or Intel® HD Graphics.
VPU: Intel® Neural Compute Stick 2 (NCS2)
FPGA: Mustang-F100-A10

Software

Usage

Local

The application can be run locally using this command:

Download the person detection model from the model zoo, which will produce .xml and .bin files.

docker run --rm -ti \
--volume "$PWD":/app \
--env DISPLAY=$DISPLAY \
--volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
mmphego/intel-openvino \
bash -c "/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py \
    --name person-detection-retail-0013"

Run inference.

xhost +;
docker run --rm -ti \
--volume "$PWD":/app \
--env DISPLAY=$DISPLAY \
--volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
mmphego/intel-openvino \
bash -c \
"source /opt/intel/openvino/bin/setupvars.sh && \
python person_detect.py
    --model models/person-detection-retail-0013 \
    --device CPU \
    --video original_videos/Manufacturing.mp4 \
    --output_path results/CPU \
    --max_people 3"
xhost -;

--env DISPLAY=$DISPLAY: Enables GUI applications
--volume="/tmp/.X11-unix:/tmp/.X11-unix:rw": Enable GUI applications

DevCloud

The Intel DevCloud offers a variety of hardware that the app could be tested on, however this assumes that you have enrolled for the Intel® Edge AI for IoT Developers or have access to the Intel DevCloud.

Note: You will need to change the PYTHON_PATH's for this to work in your environment.

Run the Jupyter notebooks

Run the different scenarios

mmphego/smart-queue-monitoring-system